Identification of Tumors

ABSTRACT

The invention provides methods for the use of gene expression measurements to classify or identify tumors in samples obtained from a subject in a clinical setting, such as in cases of formalin fixed, paraffin embedded (FFPE) samples.

RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 11/145,307, filed Jun. 3, 2005, which claims benefit of priority from U.S. Provisional Patent Application No. 60/577,084, filed Jun. 4, 2004, and both applications are incorporated in their entireties as if fully set forth.

FIELD OF THE INVENTION

This invention relates to the use of gene expression to classify human tumors. The classification is performed by use of gene expression profiles, or patterns, of 50 or more expressed sequences that are correlated with tumors arising from certain tissues as well as being correlated with certain tumor types. The invention also provides for the use of 50 or more specific gene sequences, the expression of which are correlated with tissue source and tumor type in various cancers. The gene expression profiles, whether embodied in nucleic acid expression, protein expression, or other expression formats, may be used to determine a cell containing sample as containing tumor cells of a tissue type or from a tissue origin to permit a more accurate identification of the cancer and thus treatment thereof as well as the prognosis of the subject from whom the sample was obtained.

SUMMARY OF THE INVENTION

This invention relates to the use of gene expression measurements to classify or identify tumors in cell containing samples obtained from a subject in a clinical setting, such as in cases of formalin fixed, paraffin embedded (FFPE) samples. The invention provides the ability to classify tumors in the real-world conditions faced by hospital and other laboratories which have to conduct testing on clinical FFPE samples. The invention may also be applied to other samples, such as fresh samples, that have undergone none to little or minimal treatment (such as simply storage at a reduced, non-freezing, temperature), and frozen samples. The samples may be of a primary tumor sample or of a tumor that has resulted from a metastasis of another tumor. Alternatively, the sample may be a cytological sample, such as, but not limited to, cells in a blood sample. In some cases of a tumor sample, the tumors may not have undergone classification by traditional pathology techniques, may have been initially classified but confirmation is desired, or have been classified as a “carcinoma of unknown primary” (CUP) or “tumor of unknown origin” (TUO) or “unknown primary tumor”. The need for confirmation is particularly relevant in light of the estimates of 5 to 10% misclassification using standard techniques. Thus the invention may be viewed as providing means for cancer identification, or CID.

In a first aspect of the invention, the classification is performed by use of gene expression profiles, or patterns, of 50 or more expressed sequences. The gene expression profiles, whether embodied in nucleic acid expression, protein expression, or other markers of gene expression, may be used to determine a cell containing sample as containing tumor cells of a tissue type or from a tissue origin to permit a more accurate identification of the cancer and thus treatment thereof as well as the prognosis of the subject from whom the sample was obtained.

In some embodiments, the invention is used to classify among at least 34 or at least 39 tumor types with significant accuracy in a clinical setting. The invention is based in part on the surprising and unexpected discovery that 50 or more expressed sequences in the human genome are capable of classifying among at least 34, or at least 39, tumor types, as well as subsets of those tumor types, in a meaningful manner. Stated differently, the invention is based in part on the discovery that it is not necessary to use supervised learning to identify gene sequences which are expressed in correlation with different tumor types. Thus the invention is based in part on the recognition that any 50 or more expressed sequences, even a random collection of expressed sequences, has the capability to classify, and so may be used to classify, a cell as being a tumor cell of a tissue or tissue origin.

In another aspect, the invention provides for the classifying of a cell containing sample as containing a tumor cell of a tissue type or origin by determining the expression levels of 50 or more transcribed sequences and then classifying the cell containing sample as containing a tumor cell of a plurality (two or more) of tumor types. To classify among at least 34 to at least 39 tumor types, and subsets thereof, as few as any 50 expressed sequences may be used to provide classification in a meaningful manner. The invention is also based in part on the observation that the expressed sequences need not be those the expression levels of which are evidently or highly correlated (directly, or indirectly through correlation with another expressed sequence) with any of the tumor types. Thus the invention provides, in a further embodiment, for the use of the expression levels of genes, the expression levels of which are not strongly correlated with the actual classification of the particular tumor sample, as one of the 50 or more transcribed sequences. All of the genes selected may be such non-correlates, or only a portion of the genes may be non-correlates, typically at least 90%, 85%, 75%, 50% or 25%, as well as portions falling within the ranges created by using any two of the foregoing point examples as endpoints of a range.

The invention is practiced by determining the expression levels of gene sequences where the sequences need not have been selected based on a correlation of their expression levels with the tumor types to be classified. Thus as a non-limiting example, the gene sequences need not be selected based on their correlation values with tumor types or a ranking based on the correlation values. Additionally, the invention may be practice with use of gene expression levels which are not necessarily correlated to one or more other gene expression level(s) used for classification. Thus in additional embodiments, the ability for the expression level of one expressed sequence to function in classification is not redundant with (is independent of) the ability of at least one other gene expression level used for classification.

The invention may be applied to identify the origin of a cancer in a patient in a wide variety of cases including, but not limited to, identification of the origin of a cancer in a clinical setting. In some embodiments, the identification is made by classification of a cell containing sample known to contain cancer cells, but the origin of those cells is unknown. In other embodiments, the identification is made by classification of a cell containing sample as containing one or more cancer cells followed by identification of the origin(s) of those cancer cell(s). In further embodiments, the invention is practiced with a sample from a subject with a previous history of cancer, and identification is made by classification of a cell as either being cancer from a previous origin of cancer or a new origin. Additional embodiments include those where multiple cancers found in the same organ or tissue and the invention is used to determine the origin of each cancer, as well as whether the cancers are of the same origin.

The invention is also based in part on the discovery that the expression levels of particular gene sequences can be used to classify among tumor types with greater accuracy than the expression levels of a random group of gene sequences. In one embodiment, the invention provides for the use of expression levels of 50 to 74 expressed sequences of a first set in the human genome to classify among at least 34 or at least 39 tumor types with significant accuracy. The invention thus provides for the identification and use of gene expression patterns (or profiles or “signatures”) based on the 50 to 74 expressed sequences as correlated with at least the 34 or 39 tumor types. The invention also provides for the use of 50 to 74 of these expressed sequences to classify among subsets of the 34 or 39 tumor types. Depending on the number of tumor types, accuracies ranging from over 80% to 100% may be achieved.

In another embodiment, the invention provides for the use of expression levels of 50 to 90 expressed sequences of a second set in the human genome to classify among at least 34 or at least 39 tumor types with significant accuracy. 38 of the sequences in the second set are present in the first set of 74 sequences. The expression levels of the 50 to 90 sequences in the second set may be used in the same manner as described for the first set of 74 sequences. Depending on the number of tumor types, accuracies ranging from about 75% to about 95% may be achieved.

The invention is also based in part upon the discovery that use of 50 or more expressed sequences to classify among 53 tumor types, which include (but are not limited to) the 34 and 39 types described herein, was limited by the number of available samples of some tumor types. As noted hereinbelow, accuracy is linked to the number of available samples of each tumor type such that the ability to classify additional tumor types is readily achieved by the application of increased numbers of each tumor type. Thus while the invention is exemplified by use in classifying among 34 or 39 tumor types as well as subsets of the 34 or 39, 50 or more expressed sequences can also be used to classify among all tumor types with the inclusion of samples of the additional tumor types. Thus the invention also provides for the classification of a tumor as being a type beyond the 34 or 39 types described herein.

The invention is based upon the expression levels of the gene sequences in a set of known tumor cells from different tissues and of different tumor types. These gene expression profiles (of gene sequences in the different known tumor cells/types), whether embodied in nucleic acid expression, protein expression, or other expression formats, may be compared to the expression levels of the same sequences in an unknown tumor sample to identify the sample as containing a tumor of a particular type and/or a particular origin or cell type. The invention provides, such as in a clinical setting, the advantages of a more accurate identification of a cancer and thus the treatment thereof as well as the prognosis, including survival and/or likelihood of cancer recurrence following treatment, of the subject from whom the sample was obtained.

The invention is further based in part on the discovery that use of 50 or more expressed sequences as described herein as capable of classifying among two or more tumor types necessarily and effectively eliminates one or more tumor types from consideration during classification. This reflects the lack of a need to select genes with expression levels that are highly correlated with all tumor types within the range of the classification system. Stated differently, the invention may be practiced with a plurality of genes the expression levels of which are not highly correlated with any of the individual tumor types or multiple types in the group of tumor types being classified. This is in contrast to other approaches based upon the selection and use of highly correlated genes, which likely do not “rule out” other tumor types as opposed to “rule in” a tumor type based on the positive correlation.

The classification of a tumor sample as being one of the possible tumor types described herein to the exclusion of other tumor types is of course made based upon a level of confidence as described below. Where the level of confidence is low, or an increase in the level of confidence is preferred, the classification can simply be made at the level of a particular tissue origin or cell type for the tumor in the sample. Alternatively, and where a tumor sample is not readily classified as a single tumor type, the invention permits the classification of the sample as one of a few possible tumor types described herein. This advantageously provides for the ability to reduce the number of possible tissue types, cell types, and tumor types from which to consider for selection and administration of therapy to the patient from whom the sample was obtained.

The invention thus provides a non-subjective means for the identification of the tissue source and/or tumor type of one or more cancers of an afflicted subject. Where subjective interpretation may have been previously used to determine the tissue source and/or tumor type, as well as the prognosis and/or treatment of the cancer based on that determination, the present invention provides objective gene expression patterns, which may used alone or in combination with subjective criteria to provide a more accurate identification of cancer classification. The invention is particularly advantageously applied to samples of secondary or metastasized tumors, but any cell containing sample (including a primary tumor sample) for which the tissue source and/or tumor type is preferably determined by objective criteria may also be used with the invention. Of course the ultimate determination of class may be made based upon a combination of objective and non-objective (or subjective/partially subjective) criteria.

The invention includes its use as part of the clinical or medical care of a patient. Thus in addition to using an expression profile of genes as described herein to assay a cell containing sample from a subject afflicted with cancer to determine the tissue source and/or tumor type of the cancer, the profile may also be used as part of a method to determine the prognosis of the cancer in the subject. The classification of the tumor/cancer and/or the prognosis may be used to select or determine or alter the therapeutic treatment for said subject. Thus the classification methods of the invention may be directed toward the treatment of disease, which is diagnosed in whole or in part based upon the classification. Given the diagnosis, administration of an appropriate anti-tumor agent or therapy, or the withholding or alternation of an anti-tumor agent or therapy may be used to treat the cancer.

Other clinical methods include those involved in the providing of medical care to a patient based on a classification as described herein. In some embodiments, the methods relate to providing diagnostic services based on expression levels of gene sequences, with or without inclusion of an interpretation of levels for classifying cells of a sample. In some embodiments, the method of providing a diagnostic service of the invention is preceded by a determination of a need for the service. In other embodiments, the method includes acts in the monitoring of the performance of the service as well as acts in the request or receipt of reimbursement for the performance of the service.

The details of one or more embodiments of the invention are set forth in the accompanying drawing and the description below. Other features and advantages of the invention will be apparent from the drawing and detailed description, and from the claims.

DEFINITIONS

As used herein, a “gene” is a polynucleotide that encodes a discrete product, whether RNA or proteinaceous in nature. It is appreciated that more than one polynucleotide may be capable of encoding a discrete product. The term includes alleles and polymorphisms of a gene that encodes the same product, or a functionally associated (including gain, loss, or modulation of function) analog thereof, based upon chromosomal location and ability to recombine during normal mitosis.

A “sequence” or “gene sequence” as used herein is a nucleic acid molecule or polynucleotide composed of a discrete order of nucleotide bases. The term includes the ordering of bases that encodes a discrete product (i.e. “coding region”), whether RNA or proteinaceous in nature. It is appreciated that more than one polynucleotide may be capable of encoding a discrete product. It is also appreciated that alleles and polymorphisms of the human gene sequences may exist and may be used in the practice of the invention to identify the expression level(s) of the gene sequences or an allele or polymorphism thereof. Identification of an allele or polymorphism depends in part upon chromosomal location and ability to recombine during mitosis.

The terms “correlate” or “correlation” or equivalents thereof refer to an association between expression of one or more genes and another event, such as, but not limited to, physiological phenotype or characteristic, such as tumor type.

A “polynucleotide” is a polymeric form of nucleotides of any length, either ribonucleotides or deoxyribonucleotides. This term refers only to the primary structure of the molecule. Thus, this term includes double- and single-stranded DNA and RNA. It also includes known types of modifications including labels known in the art, methylation, “caps”, substitution of one or more of the naturally occurring nucleotides with an analog, and internucleotide modifications such as uncharged linkages (e.g., phosphorothioates, phosphorodithioates, etc.), as well as unmodified forms of the polynucleotide.

The term “amplify” is used in the broad sense to mean creating an amplification product can be made enzymatically with DNA or RNA polymerases. “Amplification,” as used herein, generally refers to the process of producing multiple copies of a desired sequence, particularly those of a sample. “Multiple copies” mean at least 2 copies. A “copy” does not necessarily mean perfect sequence complementarity or identity to the template sequence. Methods for amplifying mRNA are generally known in the art, and include reverse transcription PCR (RT-PCR) and quantitative PCR (or Q-PCR) or real time PCR. Alternatively, RNA may be directly labeled as the corresponding cDNA by methods known in the art.

By “corresponding”, it is meant that a nucleic acid molecule shares a substantial amount of sequence identity with another nucleic acid molecule. Substantial amount means at least 95%, usually at least 98% and more usually at least 99%, and sequence identity is determined using the BLAST algorithm, as described in Altschul et al. (1990), J. Mol. Biol. 215:403-410 (using the published default setting, i.e. parameters w=4, t=17).

A “microarray” is a linear or two-dimensional or three dimensional (and solid phase) array of discrete regions, each having a defined area, formed on the surface of a solid support such as, but not limited to, glass, plastic, or synthetic membrane. The density of the discrete regions on a microarray is determined by the total numbers of immobilized polynucleotides to be detected on the surface of a single solid phase support, such as of at least about 50/cm², at least about 100/cm², or at least about 500/cm², up to about 1,000/cm² or higher. The arrays may contain less than about 500, about 1000, about 1500, about 2000, about 2500, or about 3000 immobilized polynucleotides in total. As used herein, a DNA microarray is an array of oligonucleotide or polynucleotide probes placed on a chip or other surfaces used to hybridize to amplified or cloned polynucleotides from a sample. Since the position of each particular group of probes in the array is known, the identities of a sample polynucleotides can be determined based on their binding to a particular position in the microarray. As an alternative to the use of a microarray, an array of any size may be used in the practice of the invention, including an arrangement of one or more position of a two-dimensional or three dimensional arrangement in a solid phase to detect expression of a single gene sequence. In some embodiments, a microarray for use with the present invention may be prepared by photolithographic techniques (such as synthesis of nucleic acid probes on the surface from the 3′ end) or by nucleic synthesis followed by deposition on a solid surface.

Because the invention relies upon the identification of gene expression, some embodiments of the invention determine expression by hybridization of mRNA, or an amplified or cloned version thereof, of a sample cell to a polynucleotide that is unique to a particular gene sequence. Polynucleotides of this type contain at least about 16, at least about 18, at least about 20, at least about 22, at least about 24, at least about 26, at least about 28, at least about 30, or at least about 32 consecutive basepairs of a gene sequence that is not found in other gene sequences. The term “about” as used in the previous sentence refers to an increase or decrease of 1 from the stated numerical value. Other embodiments are polynucleotides of at least or about 50, at least or about 100, at least about or 150, at least or about 200, at least or about 250, at least or about 300, at least or about 350, at least or about 400, at least or about 450, or at least or about 500 consecutive bases of a sequence that is not found in other gene sequences. The term “about” as used in the preceding sentence refers to an increase or decrease of 10% from the stated numerical value. Longer polynucleotides may of course contain minor mismatches (e.g. via the presence of mutations) which do not affect hybridization to the nucleic acids of a sample. Such polynucleotides may also be referred to as polynucleotide probes that are capable of hybridizing to sequences of the genes, or unique portions thereof, described herein. Such polynucleotides may be labeled to assist in their detection. The sequences may be those of mRNA encoded by the genes, the corresponding cDNA to such mRNAs, and/or amplified versions of such sequences. In some embodiments of the invention, the polynucleotide probes are immobilized on an array, other solid support devices, or in individual spots that localize the probes.

In other embodiments of the invention, all or part of a gene sequence may be amplified and detected by methods such as the polymerase chain reaction (PCR) and variations thereof, such as, but not limited to, quantitative PCR (Q-PCR), reverse transcription PCR (RT-PCR), and real-time PCR (including as a means of measuring the initial amounts of mRNA copies for each sequence in a sample), optionally real-time RT-PCR or real-time Q-PCR. Such methods would utilize one or two primers that are complementary to portions of a gene sequence, where the primers are used to prime nucleic acid synthesis. The newly synthesized nucleic acids are optionally labeled and may be detected directly or by hybridization to a polynucleotide of the invention. The newly synthesized nucleic acids may be contacted with polynucleotides (containing sequences) of the invention under conditions which allow for their hybridization. Additional methods to detect the expression of expressed nucleic acids include RNAse protection assays, including liquid phase hybridizations, and in situ hybridization of cells.

Alternatively, and in further embodiments of the invention, gene expression may be determined by analysis of expressed protein in a cell sample of interest by use of one or more antibodies specific for one or more epitopes of individual gene products (proteins), or proteolytic fragments thereof, in said cell sample or in a bodily fluid of a subject. The cell sample may be one of breast cancer epithelial cells enriched from the blood of a subject, such as by use of labeled antibodies against cell surface markers followed by fluorescence activated cell sorting (FACS). Such antibodies may be labeled to permit their detection after binding to the gene product. Detection methodologies suitable for use in the practice of the invention include, but are not limited to, immunohistochemistry of cell containing samples or tissue, enzyme linked immunosorbent assays (ELISAs) including antibody sandwich assays of cell containing tissues or blood samples, mass spectroscopy, and immuno-PCR.

The terms “label” or “labeled” refer to a composition capable of producing a detectable signal indicative of the presence of the labeled molecule. Suitable labels include radioisotopes, nucleotide chromophores, enzymes, substrates, fluorescent molecules, chemiluminescent moieties, magnetic particles, bioluminescent moieties, and the like. As such, a label is any composition detectable by spectroscopic, photochemical, biochemical, immunochemical, electrical, optical or chemical means.

The term “support” refers to conventional supports such as beads, particles, dipsticks, fibers, filters, membranes and silane or silicate supports such as glass slides.

“Expression” and “gene expression” include transcription and/or translation of nucleic acid material.

As used herein, the term “comprising” and its cognates are used in their inclusive sense; that is, equivalent to the term “including” and its corresponding cognates.

Conditions that “allow” an event to occur or conditions that are “suitable” for an event to occur, such as hybridization, strand extension, and the like, or “suitable” conditions are conditions that do not prevent such events from occurring. Thus, these conditions permit, enhance, facilitate, and/or are conducive to the event. Such conditions, known in the art and described herein, depend upon, for example, the nature of the nucleotide sequence, temperature, and buffer conditions. These conditions also depend on what event is desired, such as hybridization, cleavage, strand extension or transcription.

Sequence “mutation,” as used herein, refers to any sequence alteration in the sequence of a gene disclosed herein interest in comparison to a reference sequence. A sequence mutation includes single nucleotide changes, or alterations of more than one nucleotide in a sequence, due to mechanisms such as substitution, deletion or insertion. Single nucleotide polymorphism (SNP) is also a sequence mutation as used herein. Because the present invention is based on the relative level of gene expression, mutations in non-coding regions of genes as disclosed herein may also be assayed in the practice of the invention.

“Detection” or “detecting” includes any means of detecting, including direct and indirect determination of the level of gene expression and changes therein.

Unless defined otherwise all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this invention belongs.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a capacity plot for the ability to use the expression levels of subsets of a set of 100 expressed gene sequences to classify among 39 tumor types and subsets thereof. Expression levels of random combinations of 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 and 100 (each sampled 10 times) of the 100 sequences were used with data from tumor types and then used to predict test random sets of tumor samples (each sampled 10 times) ranging from 2 to 39 types. A plot of numbers of tumor types versus prediction accuracies for results using from 50 to 100 genes are shown as non-limiting examples. Generally, accuracy improves with higher numbers of gene sequences, where 50 gene sequences results in a more noticeable reduction in accuracy when used with about 20 or more tumor types.

FIG. 2 shows an alternative presentation of the data used with respect to FIG. 1. A plot of numbers of gene sequences used, ranging from 50-100, versus prediction accuracies for various representative numbers of tumor types is shown. The plotted lines, from top to bottom, are of the results from 2, 10, 20, 30, and 39 tumor types, respectively.

FIG. 3 shows the performance of using all genes from a first set of 74 gene sequences and a second set of 90 gene sequences to classify various numbers of tumor types. Generally, the accuracy of the two sets are very similar, with the set of 74 displaying a more noticeable higher accuracy with about 28 or more (up to 39) tumor types.

FIG. 4 shows a capacity plot for the ability to use the expression levels of all or portions of a first set of 74 expressed gene sequences to classify among 39 tumor types and subsets thereof. Expression levels of random combinations of 50, 55, 60, 65, and 70 (each sampled 10 times) as well as all 74 of the sequences were used with data from tumor types and then used to predict test random sets of tumor samples (each sampled 10 times) ranging from 2 to 39 types. A plot of numbers of tumor types versus prediction accuracies for results using from 50 to 74 genes are shown as non-limiting examples. Generally, accuracy improves with higher numbers of gene sequences, with the use of 74 genes being more noticeable as providing the highest accuracies, and 50 gene sequences producing the lowest accuracies, when used with about 20 or more tumor types.

FIG. 5 shows an alternative presentation of the data used with respect to FIG. 4. A plot of numbers of gene sequences used, ranging from 50-74, versus prediction accuracies for various representative numbers of tumor types is shown. The plotted lines, from top to bottom, are of the results from 2, 10, 20, 30, and 39 tumor types, respectively.

FIG. 6 shows a capacity plot for the ability to use the expression levels of subsets of a set of 90 expressed gene sequences to classify among 39 tumor types and subsets thereof. Expression levels of random combinations of 50, 55, 60, 65, 70, 75, 80, and 85 (each sampled 10 times) as well as all 90 of the sequences were used with data from tumor types and then used to predict test random sets of tumor samples (each sampled 10 times) ranging from 2 to 39 types. A plot of numbers of tumor types versus prediction accuracies for results using from 50 to 90 genes are shown as non-limiting examples. Generally, accuracy improves with higher numbers of gene sequences, where 50 gene sequences results in noticeably reduced accuracy when used with about 20 or more tumor types.

FIG. 7 shows an alternative presentation of the data used with respect to FIG. 6. A plot of numbers of gene sequences used, ranging from 50-90, versus prediction accuracies for various representative numbers of tumor types is shown. The plotted lines, from top to bottom, are of the results from 2, 10, 20, 30, and 39 tumor types, respectively.

FIGS. 8A-8D show a “tree” that classifies tumor types covered herein as well as additional known tumor types. It was constructed mainly according to “Cancer, Principles and Practice of Oncology, (DeVito, Hellman and Rosenberg), 6^(th) edition”. Thus beginning with a “tumor of unknown origin” (or “tuo”), the first possibilities are that it is either of a germ cell or non-germ cell origin. If it is the former, then it may be of ovary or testes origin. Within those of testes origin, the tumor may be of seminoma origin or an “other” origin.

If the tumor is of a non-germ cell origin, then it is either of a epithelial or non-epithelial origin. If it is the former, then it is either squamous or non-squamous origin. Squamous origin tumors are of cervix, esophagus, larynx, lung, or skin in origin. Non-squamous origin tumors are of urinary bladder, breast, carcinoid-intestine, cholangiocarcinoma, digestive, kidney, liver, lung, prostate, reproductive system, skin-basal cell, or thyroid-follicular-papillary origin. Among those of digestive origin, the tumors are of small and large bowel, stomach-adenocarcinoma, bile duct, esophagus, gall bladder, and pancreas in origin. The esophagus origin tumors may be of either Barrett's esophagus or adenocarcinoma types. Of the reproductive system origin tumors, they may be of cervix adenocarcinoma type, endometrial tumor, or ovarian origin. Ovarian origin tumors are of the clear, serous, mucinous, and endometroid types.

If the tumor is of non-epithelial origin, then it is of adrenal gland, brain, GIST (gastrointestinal stromal tumor), lymphoma, meningioma, mesothelioma, sarcoma, skin melanoma, or thyroid-medullary origin. Of the lymphomas, they are B cell, Hodgkin's, or T cell type. Of the sarcomas, they are leimyosarcoma, osteosarcoma, soft-tissue sarcoma, soft tissue MFH (malignant fibrous histiocytoma), soft tissue sarcoma synovial, soft tissue Ewing's sarcoma, soft tissue fibrosarcoma, and soft tissue rhabdomyosarcoma types.

DETAILED DESCRIPTION OF MODES OF PRACTICING THE INVENTION

This invention provides methods for the use of gene expression information to classify tumors in a more objective manner than possible with conventional pathology techniques. The invention is based in part on the results of randomly reducing the number of gene sequences used to classify a tumor sample as one of a plurality of tumor types, such as the 34 tumor types described below and in U.S. Provisional Application 60/577,084, filed Jun. 4, 2004. A total number of 16,948 genes, which were filtered down from a larger set based upon removal of genes that display low or constant signals in the samples used was used for both cross-validation and prediction accuracies as described in the examples below. 100 random selections of 50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 1200, 1400, 1600, 1800, 2000, 2200, 2400, 2600, 2800, 3000 and more genes from the total were selected and used for classification as described herein.

Thus in a first aspect, the invention provides a method of classifying a cell containing sample as including a tumor cell of (or from) a type of tissue or a tissue origin. The method comprises determining or measuring the expression levels of 50 or more transcribed sequences from cells in a cell containing sample obtained from a subject, and classifying the sample as containing tumor cells of a type of tissue from a plurality of tumor types based on the expression levels of said sequences. As used herein, “a plurality” refers to the state of two or more.

In some embodiments of the invention, the expression of more than 50% of said transcribes sequences are not correlated with expression of another one of said transcribed sequences; and/or the 50 or more transcribes sequences are not selected based upon supervised learning using known tumor samples, on the level of correlation between their expression and said plurality of tumor types, or on their rank in a correlation between their expression and said plurality of tumor types.

The classifying is based upon a comparison of the expression levels of the 50 or more transcribed sequences in the cells of the sample to their expression levels in known tumor samples and/or known non-tumor samples. Alternatively, the classifying is based upon a comparison of the expression levels of the 50 or more transcribed sequences to the expression of reference sequences in the same samples, relative to, or based on, the same comparison in known tumor samples and/or known non-tumor samples. Thus as a non-limiting example, the expression levels of the gene sequences may be determined in a set of known tumor samples to provide a database against which the expression levels detected or determined in a cell containing sample from a subject is compared. The expression level(s) of gene sequence(s) in a sample also may be compared to the expression level(s) of said sequence(s) in normal or non-cancerous cells, preferably from the same sample or subject. As described below and in embodiments of the invention utilizing Q-PCR or real time Q-PCR, the expression levels may be compared to expression levels of reference genes in the same sample or a ratio of expression levels may be used.

The selection of 50 or more gene sequences to use may be random, or by selection based on various criteria. As one non-limiting example, the gene sequences may be selected based upon unsupervised learning, including clustering techniques. As another non-limiting example, selection may be to reduce or remove redundancy with respect to their ability to classify tumor type. For example, gene sequences are selected based upon the lack of correlation between their expression and the expression of one or more other gene sequences used for classifying. This is accomplished by assessing the expression level of each gene sequence in the expression data set for correlation, across the plurality of samples, with the expression level of each other gene in the data set to produce a correlation matrix of correlation coefficients. These correlation determinations may be performed directly, between expression of each pair of gene sequences, or indirectly, without direct comparison between the expression values of each pair of gene sequences.

A variety of correlation methodologies may be used in the correlation of expression data of individual gene sequences within the data set. Non-limiting examples include parametric and non-parametric methods as well as methodologies based on mutual information and non-linear approaches. Non-limiting examples of parametric approaches include Pearson correlation (or Pearson r, also referred to as linear or product-moment correlation) and cosine correlation. Non-limiting examples of non-parametric methods include Spearman's R (or rank-order) correlation, Kendall's Tau correlation, and the Gamma statistic. Each correlation methodology can be used to determine the level of correlation between the expressions of individual gene sequences in the data set. The correlation of all sequences with all other sequences is most readily considered as a matrix. Using Pearson's correlation as a non-limiting example, the correlation coefficient r in the method is used as the indicator of the level of correlation. When other correlation methods are used, the correlation coefficient analogous to r may be used, along with the recognition of equivalent levels of correlation corresponding to r being at or about 0.25 to being at or about 0.5.

The correlation coefficient may be selected as desired to reduce the number of correlated gene sequences to various numbers. In some embodiments of the invention using r, the selected coefficient value may be of about 0.25 or higher, about 0.3 or higher, about 0.35 or higher, about 0.4 or higher, about 0.45 or higher, or about 0.5 or higher. The selection of a coefficient value means that where expression between gene sequences in the data set is correlated at that value or higher, they are possibly not included in a subset of the invention. Thus in some embodiments, the method comprises excluding or removing (not using for classification) one or more gene sequences that are expressed in correlation, above a desired correlation coefficient, with another gene sequence in the tumor type data set. It is pointed out, however, that there can be situations of gene sequences that are not correlated with any other gene sequences, in which case they are not necessarily removed from use in classification.

Thus the expression levels of gene sequences, where more than about 10%, more than about 20%, more than about 30%, more than about 40%, more than about 50%, more than about 60%, more than about 70%, more than about 80%, or more than about 90% of the levels are not correlated with that of another one of the gene sequences used, may be used in the practice of the invention. Correlation between expression levels may be based upon a value below about 0.9, about 0.8, about 0.7, about 0.6, about 0.5, about 0.4, about 0.3, or about 0.2. The ability to classify among classes with exclusion of the expression levels of some gene sequences is present because expression of the gene sequences in the subset is correlated with expression of the gene sequences excluded from the subset. So no information was lost because information based on the expression of the excluded gene sequences is still represented by sequences retained in the subset. Therefore, expression of the gene sequences of the subset has information content relevant to properties and/or characteristics (or phenotype) of a cell. This has application and relevance to the classification of additional tumor type classes not included as part of the original gene expression data set which can be classified by use of a subset of the invention because based on the redundancy of information between expression of sequences in the subset and sequences expressed in those additional classes. Thus the invention may be used to classify cells as being a tumor type beyond the plurality of known classes used to generate the original gene expression data set.

Selection of gene sequences based upon reducing correlation of expression to a particular tumor type may also be used. This also reflects a discovery of the present invention, based upon the observation that expression levels that were most highly correlated with one or more tumor types was not necessarily of greatest value in classification among different tumor types. This is reflected both by the ability to use randomly selected gene sequences for classification as well as the use of particular sequences, as described herein, which are not expressed with the most significant correlation with one or more tumor types. Thus the invention may be practiced without selection of gene sequences based upon the most significant P values or a ranking based upon correlation of gene expression and one or more tumor types. Thus the invention may be practiced without the use of ranking based methodologies, such as the Kruskal-Wallis H-test.

The gene sequences used in the practice of the invention may include those which have been observed to be expressed in correlation with particular tumor types, such as expression of the estrogen receptor, which has been observed to be expressed in correlation with some breast and ovarian cancers. In some embodiments of the invention, however, the invention is practiced with use of the expression level of at least one gene sequence that has not been previously identified as being associated with any of the tumor types being classified. Thus the invention may be practiced without all of the gene sequences having previously been associated or correlated with expression in the 2 or more (up to 39 or more) tumor types to which a cell containing sample may be classified.

While the invention is described mainly with respect to human subjects, samples from other subjects may also be used. All that is necessary is the ability to assess the expression levels of gene sequences in a plurality of known tumor samples such that the expression levels in an unknown or test sample may be compared. Thus the invention may be applied to samples from any organism for which a plurality of expressed sequences, and a plurality of known tumor samples, are available. One non-limiting example is application of the invention to mouse samples, based upon the availability of the mouse genome to permit detection of expressed murine sequences and the availability of known mouse tumor samples or the ability to obtain known samples. Thus, the invention is contemplated for use with other samples, including those of mammals, primates, and animals used in clinical testing (such as rats, mice, rabbits, dogs, cats, and chimpanzees) as non-limiting examples.

While the invention is readily practiced with the use of cell containing samples, any nucleic acid containing sample which may be assayed for gene expression levels may be used in the practice of the invention. Without limiting the invention, a sample of the invention may be one that is suspected or known to contain tumor cells. Alternatively, a sample of the invention may be a “tumor sample” or “tumor containing sample” or “tumor cell containing sample” of tissue or fluid isolated from an individual suspected of being afflicted with, or at risk of developing, cancer. Non-limiting examples of samples for use with the invention include a clinical sample, such as, but not limited to, a fixed sample, a fresh sample, or a frozen sample. The sample may be an aspirate, a cytological sample (including blood or other bodily fluid), or a tissue specimen, which includes at least some information regarding the in situ context of cells in the specimen, so long as appropriate cells or nucleic acids are available for determination of gene expression levels. The invention is based in part on the discovery that results obtained with frozen tissue sections can be validly applied to the situation with fixed tissue or cell samples and extended to fresh samples.

Non-limiting examples of fixed samples include those that are fixed with formalin or formaldehyde (including FFPE samples), with Boudin's, glutaldehyde, acetone, alcohols, or any other fixative, such as those used to fix cell or tissue samples for immunohistochemistry (IHC). Other examples include fixatives that precipitate cell associated nucleic acids and proteins. Given possible complications in handling frozen tissue specimens, such as the need to maintain its frozen state, the invention may be practiced with non-frozen samples, such as fixed samples, fresh samples, including cells from blood or other bodily fluid or tissue, and minimally treated samples. In some applications of the invention, the sample has not been classified using standard pathology techniques, such as, but not limited to, immunohistochemistry based assays.

In some embodiments of the invention, the sample is classified as containing a tumor cell of a type selected from the following 53, and subsets thereof: Adenocarcinoma of Breast, Adenocarcinoma of Cervix, Adenocarcinoma of Esophagus, Adenocarcinoma of Gall Bladder, Adenocarcinoma of Lung, Adenocarcinoma of Pancreas, Adenocarcinoma of Small-Large Bowel, Adenocarcinoma of Stomach, Astrocytoma, Basal Cell Carcinoma of Skin, Cholangiocarcinoma of Liver, Clear Cell Adenocarcinoma of Ovary, Diffuse Large B-Cell Lymphoma, Embryonal Carcinoma of Testes, Endometrioid Carcinoma of Uterus, Ewings Sarcoma, Follicular Carcinoma of Thyroid, Gastrointestinal Stromal Tumor, Germ Cell Tumor of Ovary, Germ Cell Tumor of Testes, Glioblastoma Multiforme, Hepatocellular Carcinoma of Liver, Hodgkin's Lymphoma, Large Cell Carcinoma of Lung, Leiomyosarcoma, Liposarcoma, Lobular Carcinoma of Breast, Malignant Fibrous Histiocytoma, Medulary Carcinoma of Thyroid, Melanoma, Meningioma, Mesothelioma of Lung, Mucinous Adenocarcinoma of Ovary, Myofibrosarcoma, Neuroendocrine Tumor of Bowel, Oligodendroglioma, Osteosarcoma, Papillary Carcinoma of Thyroid, Pheochromocytoma, Renal Cell Carcinoma of Kidney, Rhabdomyosarcoma, Seminoma of Testes, Serous Adenocarcinoma of Ovary, Small Cell Carcinoma of Lung, Squamous Cell Carcinoma of Cervix, Squamous Cell Carcinoma of Esophagus, Squamous Cell Carcinoma of Larynx, Squamous Cell Carcinoma of Lung, Squamous Cell Carcinoma of Skin, Synovial Sarcoma, T-Cell Lymphoma, and Transitional Cell Carcinoma of Bladder.

In other embodiments of the invention, the sample is classified as containing a tumor cell of a type selected from the following 34, and subsets thereof: adrenal, brain, breast, carcinoid-intestine, cervix (squamous cell), cholangiocarcinoma, endometrium, germ-cell, GIST (gastrointestinal stromal tumor), kidney, leiomyosarcoma, liver, lung (adenocarcinoma, large cell), lung (small cell), lung (squamous), lymphoma (B cell), Lymphoma (Hodgkins), meningioma, mesothelioma, osteosarcoma, ovary (clear cell), ovary (serous cell), pancreas, prostate, skin (basal cell), skin (melanoma), small and large bowel; soft tissue (liposarcoma); soft tissue (MFH or Malignant Fibrous Histiocytoma), soft tissue (Sarcoma-synovial), testis (seminoma), thyroid (follicular-papillary), thyroid (medullary carcinoma), and urinary bladder.

In further embodiments of the invention, the sample is classified as containing a tumor cell of a type selected from the following 39, and subsets thereof: adrenal gland, brain, breast, carcinoid-intestine, cervix-adenocarcinoma, cervix-squamous, endometrium, gall bladder, germ cell-ovary, GIST, kidney, leiomyosarcoma, liver, lung-adenocarcinoma-large cell, lung-small cell, lung-squamous, lymphoma-B cell, lymphoma-Hodgkin's, lymphoma-T cell, meningioma, mesothelioma, osteosarcoma, ovary-clear cell, ovary-serous, pancreas, prostate, skin-basal cell, skin-melanoma, skin-squamous, small and large bowel, soft tissue-liposarcoma, soft tissue-MFH, soft tissue-sarcoma-synovial, stomach-adenocarcinoma, testis-other (or non-seminoma), testis-seminoma, thyroid-follicular-papillary, thyroid-medullary, and urinary bladder.

The methods of the invention may also be applied to classify a cell containing sample as containing a tumor cell of a tumor of a subset of any of the above sets. The size of the subset will usually be small, composed of two, three, four, five, six, seven, eight, nine, or ten of the tumor types described above. Alternatively, the size of the subset may be any integral number up to the full size of the set. Thus embodiments of the invention include classification among 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, or 52 of the above types. In some embodiments, the subset will be composed of tumor types that are of the same tissue or organ type. Alternatively, the subset will be composed of tumor types of different tissues or organs. In some embodiments, the subset will include one or more types selected from adrenal gland, brain, carcinoid-intestine, cervix-adenocarcinoma, cervix-squamous, gall bladder, germ cell-ovary, GIST, leiomyosarcoma, liver, meningioma, osteosarcoma, skin-basal cell, skin-squamous, soft tissue-liposarcoma, soft tissue-MFH, soft tissue-sarcoma-synovial, testis-other (or non-seminoma), testis-seminoma, thyroid-follicular-papillary, and thyroid-medullary.

Classification among subsets of the above tumor types is demonstrated by the results shown in FIGS. 1 and 2, where the expression levels of as few as 50 or more genes sequences can be used to classify among random samples of 2 tumor types among those in the set of 39 listed above. Expression levels of 50-100 gene sequences (that were randomly selected) can be used to classify among 2 to 39 tumor types with varying degrees of accuracy. The invention may be practiced with the expression levels of 50 or more, about 55 or more, about 60 or more, about 65 or more, about 70 or more, about 75 or more, about 80 or more, about 85 or more, about 90 or more, about 100 or more, about 110 or more, about 120 or more, about 130 or more, about 140 or more, about 150 or more, about 200 or more, about 250 or more, about 300 or more, about 350 or more, or about 400 or more transcribed sequences as found in the human “transcriptome” (transcribed portion of the genome). The invention may also be practiced with expression levels of 50-60 or more, about 60-70 or more, about 70-80 or more, about 80-90 or more, about 90-100 or more, about 100-110 or more, about 110-120 or more, about 120-130 or more, or about 130-140 or more transcribed sequences. In some embodiments of the invention, the transcribed genes may be randomly picked or include all or some of the specific genes sequences disclosed herein. As demonstrated herein, classification with accuracies of about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, or about 95% or higher can be performed by use of the instant invention.

In other embodiments, the gene expression levels of other gene sequences may be determined along with the above described determinations of expression levels for use in classification. One non-limiting example of this is seen in the case of a microarray based platform to determine gene expression, where the expression of other gene sequences is also measured. Where those other expression levels are not used in classification, they may be considered the results of “excess” transcribed sequences and not critical to the practice of the invention. Alternatively, and where those other expression levels are used in classification, they are within the scope of the invention, where the description of using particular numbers of sequences does not necessarily exclude the use of expression levels of additional sequences. In some embodiments, the invention includes the use of expression level(s) from one or more “excess” gene sequences, such as those which may provide information redundant to one or more other gene sequences used in a method of the invention.

Because classification of a sample as containing cells of one of the above tumor types inherently also classifies the tissue or organ site origin of the sample, the methods of the invention may be applied to classification of a tumor sample as being of a particular tissue or organ site of the patient. This application of the invention is particularly useful in cases where the sample is of a tumor that is the result of metastasis by another tumor. In some embodiments of the invention, the tumor sample is classified as being one of the following 24: Adrenal, Bladder, Bone, Brain, Breast, Cervix, Endometrium, Esophagus, Gall Bladder, Kidney, Larynx, Liver, Lung, Lymph Node, Ovary, Pancreas, Prostate, Skin, Soft Tissue, Small/Large Bowel, Stomach, Testes, Thyroid, and Uterus.

While the invention also provides for classification as one of the above tumor types based upon comparisons to the expression levels of sequences in the 39 tumor types, it is possible that a higher level of confidence in the classification is desired. If an increase in the confidence of the classification is preferred, the classification can be adjusted to identify the tumor sample as being of a particular origin or cell type as shown in FIG. 8. Thus an increase in confidence can be made in exchange for a decrease in specificity as to tumor type by identification of origin or cell type.

The classification of a cell containing sample as having a tumor cell of one of the 39 tumor types above inherently also classifies the tissue or organ site origin of the sample. For example, the identification of a sample as being cervix-squamous necessarily classifies the tumor as being of cervical origin, squamous cell type (and thus epithelial rather than non-epithelial in origin) as shown in FIG. 8. It also means that the tumor was necessarily not germ cell in origin. Thus, the methods of the invention may be applied to classification of a tumor sample as being of a particular tissue or organ site of a subject or patient. This application of the invention is particularly useful in cases where the sample is of a tumor that is the result of metastasis by another tumor.

The practice of the invention to classify a cell containing sample as having a tumor cell of one of the above types is by use of an appropriate classification algorithm that utilizes supervised learning to accept 1) the levels of expression of the gene sequences in a plurality of known tumor types as a training set and 2) the levels of expression of the same genes in one or more cells of a sample to classify the sample as having cells of one of the tumor types. Further discussion of this is provided in the Example section herein. The levels of expression may be provided based upon the signals in any format, including nucleic acid expression or protein expression as described herein.

As would be evident to the skilled practitioner, the range of classification is affected by the number of tumor types as well as the number of samples for each tumor type. But given adequate samples of the full range of human tumors as provided herein, the invention is readily applied to the classification of those tumor types as well as additional types.

Non-limiting examples of classification algorithms that may be used in the practice of the invention include supervised learning algorithms, machine learning algorithms, linear discriminant analysis, attribute selection algorithms, and artificial neural networks (ANN). In preferred embodiments of the invention, a distance-based classification algorithm, such as the k-nearest neighbor (KNN) algorithm, or support vector machine (SVM) are used.

The use of KNN is in some embodiments of the invention and is discussed further as a non-limiting representative example. KNN can be used to analyze the expression data of the genes in a “training set” of known tumor samples including all 39 of the tumor types described herein. The training data set can then be compared to the expression data for the same genes in a cell containing sample. The expression levels of the genes in the sample are then compared to the training data set via KNN to identify those tumor samples with the most similar expression patterns. As a non-limiting example, the five “nearest neighbors” may be identified and the tumor types thereof used to classify the unknown tumor sample. Of course other numbers of “nearest neighbors” may be used. Non-limiting examples include less than 5, about 7, about 9, or about 11 or more “nearest neighbors”.

As a hypothetical example, if the five “nearest neighbors” of an unknown sample are four B cell lymphomas and one T cell lymphoma, then the classification of the sample as being of a B cell lymphoma can be made with great accuracy. This has been used with 84% or greater accuracy, such as 90%, as described in the Examples.

The classification ability may be combined with the inherent nature of the classification scheme to provide a means to increase the confidence of tumor classification in certain situations. For example, if the five “nearest neighbors” of a sample are three ovary clear cell and two ovary serous tumors, confidence can be improved by simply treating the tumors as being of ovarian origin and treating the subject or patient (from whom the sample was obtained) accordingly. See FIG. 8. This is an example of trading off specificity in favor of increased confidence. This provides the added benefit of addressing the possibility that the unknown sample was a mucinous or endometroid tumor. Of course the skilled practitioner is free to treat the tumor as one or both of these two most likely possibilities and proceeding in accordance with that determination.

Because the developmental lineage of tumor cells in certain tumor types (e.g., germ cells) can be complex and involve multiple cell types, FIG. 8 may appear to be oversimplified. However, it serves as a good basis to relate known histopathology and to serve as a “guide tree” for analyzing and relating tumor-associated gene expression signatures.

The inherent nature of the classification scheme also provides a means to increase the confidence of tumor classification in cases wherein the “nearest neighbors” are ambiguous. For example, if the five “nearest neighbors” were one urinary bladder, one breast, one kidney, one liver, and one prostate, the classification can simply be that of a non-squamous cell tumor. Such a determination can be made with significant confidence and the subject or patient from whom the sample was obtained can be treated accordingly. Without being bound by theory, and offered solely to improve the understanding of the invention, the last two examples reflect the similarities in gene expression of cells of a similar cell type and/or tissue origin.

Embodiments of the invention include use of the methods and materials described herein to identify the origin of a cancer from a patient. Thus given a sample containing tumor cells, the tissue origin of the tumor cells is identified by use of the present invention. One non-limiting example is in the case of a subject with an inflamed lymph node containing cancer cells. The cells may be from a tissue or organ that drains into the lymph node or it may be from another tissue source. The present invention may be used to classify the cells as being of a particular tumor or tissue type (or origin) which allows the identification of the source of the cancer cells. In an alternative non-limiting example, the sample (such as that from a lymph node) contains cells, which are first assayed by use of the invention to classify at least one cell as being a tumor cell of a tissue type or origin. This is then used to identify the source of the cancer cells in the sample. Both of these are examples of the advantageous use of the invention to save time, effort, and cost in the use of other cancer diagnostic tests.

In further embodiments, the invention is practiced with a sample from a subject with a previous history of cancer. As a non-limiting example, a cell containing sample (from the lymph node or elsewhere) of the subject may be found to contain cancer cells such that the present invention may be used to determine whether the cells are from the same or a different tissue from that of the previous cancer. This application of the invention may also be used to identify a new primary tumor, such as the case where new cancer cells are found in the liver of a subject who previously had breast cancer. The invention may be used to identify the new cancer cells as being the result of metastasis from the previous breast cancer (or from another tumor type, whether previously identified or not) or as a new primary occurrence of liver cancer. The invention may also be applied to samples of a tissue or organ where multiple cancers are found to determine the origin of each cancer, as well as whether the cancers are of the same origin.

While the invention may be practiced with the use of expression levels of a random group of expressed gene sequences, the invention also provides exemplary gene sequences for use in the practice of the invention. The invention includes a first group of 74 gene sequences from which 50 or more may be used in the practice of the invention. The 50 to 74 gene sequences may be used along with the determination of expression levels of additional sequences so long as the expression levels of gene sequences from the set of 74 are used in classifying. A non-limiting example of such embodiments of the invention is where the expression of the 74 gene sequences, or at least 50 (or 50 to about 90) members thereof, is measured along with the expression levels of a plurality of other sequences, such as by use of a microarray based platform used to perform the invention. Where those other expression levels are not used in classification, they may be considered the results of “excess” transcribed sequences and not critical to the practice of the invention. Alternatively, and where those other expression levels are used in classification, they are within the scope of the invention, where the use of the above described sequences does not necessarily exclude the use of expression levels of additional sequences.

mRNA sequences corresponding to a set of 74 gene sequences for use in the practice of the invention are provided in the attached Sequence Listing. A listing of the SEQ ID NOs, with corresponding identifying information, including accession numbers and other information, is provided by the following.

(SEQ ID NO: 1) >Hs.73995_mRNA_1 gi|190403|gb|M60502.1|HUMPROFILE Human profilaggrin mRNA, 3′ end polyA = 1 (SEQ ID NO: 2) >Hs.75236_mRNA_4 gi|14280328|gb|AY033998.1| Homo sapiens polyA = 3 (SEQ ID NO: 3) >Hs.299867_mRNA_1 gi|4758533|ref|NM_004496.1| Homo sapiens hepatocyte nuclear factor 3, alpha (HNF3A), mRNA polyA = 3 >Hs.285401_contig1 (SEQ ID NO: 4) AI147926|AI880620|AA768316|AA761543|AA279147|AI216016|AI738663|N79248|AI684 489|AA960845|AI718599|AI379138|N29366|BF002507|AW044269|R34339|R66326|H0464 8|R67467|AI523112|BF941500 polyA = 2 polyA = 3 (SEQ ID NO: 5) >Hs.182507_mRNA_1 gi|15431324|ref|NM_002283.2| Homo sapiens keratin, hair, basic, 5 (KRTHB5), mRNA polyA = 3 >Hs.292653_contig1 (SEQ ID NO: 6) AI200660|AW014007|AI341199|AI692279|AI393765|AI378686|AI695373|AW292108|T10 352|R44346|AW470408|AI380925|BF938983|AW003704|H08077|F03856|H08075|F08895| AW468398|AI865976|H22568|AI858374|AI216499 polyA = 2 polyA = 3 (SEQ ID NO: 7) >Hs.97616_mRNA_3 gi|12654852|gb|BC001270.1|BC001270 Homo sapiens clone MGC: 5069 IMAGE: 3458016 polyA = 3 (SEQ ID NO: 8) >Hs.123078_mRNA_3 gi|14328043|gb|BC009237.1|BC009237 Homo sapiens clone MGC: 2216 IMAGE: 2989823 polyA = 3 (SEQ ID NO: 9) >Hs.285508_contig1 AW194680|BF939744|BF516467 polyA = 1 polyA = 1 >Hs.183274_contig1 (SEQ ID NO: 10) BF437393|BF64008|BF509951|AW134603|AI277015|AI803254|AA887915|BF054958|AI0 04413|AI393911|AI278517|AW612644|AI492162|AI309226|AI863671|AA448864|AI6401 65|AA479926|AA461188|AA780161|BF591180|AI918020|AI758226|AI291375|BF001845| BF003064|AI337393|AI522206|BE856784|BF001760|AI280300 FLAG = 1 polyA = 2 WARN polyA = 3 (SEQ ID NO: 11) >Hs.334841_mRNA_3 gi|14290606|gb|BC009084.1|BC009084 Homo sapiens clone MGC: 9270 IMAGE: 3853674 polyA = 3 >Hs.3321_contig1 (SEQ ID NO: 12) AI804745|AI492375|AA594799|BE672611|AA814147|AA722404|AW170088|D11718|BG153 444|AI680648|AA063561|BE219054|AI590287|R55185|AI479167|AI796872|AI018324|A I701122|BE218203|AA905336|AI681917|BI084742|AI480008|AI217994|AI401468 polyA = 2 polyA = 3 (SEQ ID NO: 13) >Hs.306216_singlet1 AW083022 polyA = 1 polyA = 2 (SEQ ID NO: 14) >Hs.99235_contig1 AA456140|AI167259|AA450056 polyA = 2 polyA = 3 (SEQ ID NO: 15) >Hs.169172_mRNA_2 gi|2274961|emb|AJ000388.1|HSCANPX Homo sapiens mRNA for calpain-like protease CANPX polyA = 3 (SEQ ID NO: 16) >Hs.351486_mRNA_1 gi|16549178|dbj|AK054605.1|AK054605 Homo sapiens cDNA FLJ30043 fis, clone 3NB692001548 polyA = 0 >Hs.153504_contig2 (SEQ ID NO: 17) BE962007|AW016349|AW016358|AW139144|AA932969|AI025620|AI688744|AI865632|AA8 54291|AA932970|AU156702|AI634439|AA152496|AI539557|AI123490|AI613215|AI3183 63|AW105672|AA843483|AI366889|AW181938|AI813801|AI433695|AA934772|N72230|AI 760632|BE858965|AW058302|AI760087|AI682077|AA886672|AI350384|AW243848|AW300 574|BE466359|AI859529|AI921588|BF062899|BE855597|BE617708 polyA = 2 polyA = 3 (SEQ ID NO: 18) >Hs.199354_singlet1 AI669760 polyA = 1 polyA = 2 (SEQ ID NO: 19) >Hs.162020_contig1 AW291189|AA505872 polyA = 2 polyA = 3 (SEQ ID NO: 20) >Hs.30743_mRNA_3 gi|18201906|ref|NM_006115.2| Homo sapiens preferentially expressed antigen in melanoma (PRAME), mRNA polyA = 3 >Hs.271580_contig1 (SEQ ID NO: 21) AI632869|AW338882|AW338875|AW613773|AI982899|AW193151|BE206353|BE208200|AI8 11548|AW264021 polyA = 2 polyA = 3 (SEQ ID NO: 22) >Hs.69360_mRNA_2 gi|14250609|gb|BC008764.1|BC008764 Homo sapiens clone MGC: 1266 IMAGE: 3347571 polyA = 3 (SEQ ID NO: 23) >Hs.30827_contig1 H07885|N39347|W85913|AA583408|W86449 polyA = 2 polyA = 3 >Hs.211593_contig2 (SEQ ID NO: 24) BF592799|AI570478|AA234440|R40214|BE501078|AW593784|AI184050|AI284161|W7214 9|AW780437|AI247981|AW241273|H60824 polyA = 2 polyA = 3 (SEQ ID NO: 25) >Hs.155097_mRNA_1 gi|15080385|gb|BC011949.1|BC011949 Homo sapiens clone MGC: 9006 IMAGE: 3863603 polyA = 3 (SEQ ID NO: 26) >Hs.5163_mRNA_1 gi|15990433|gb|BC015582.1|BC015582 Homo sapiens clone MGC: 23280 IMAGE: 4637504 polyA = 3 (SEQ ID NO: 27) >Hs.55150_mRNA_1 gi|17068414|gb|BC017586.1|BC017586 Homo sapiens clone MGC: 26610 IMAGE: 4837506 polyA = 3 >Hs.170177_contig3 (SEQ ID NO: 28) AI620495|AW291989|AA780896|AA976262|AI298326|BF111862|AW591523|AI922518|AI4 80280|BF589437|AA600354|AI886238|AA035599|H90049|BF112011|N52601|AI570965|A I565367|AW768847|H90073|BE504361|N45292|AI632075|AA679729|AW168052|AI978827 |AI968410|AI669255|N45300|AI651256|AI698970|AI521256|AW078614|AI802070|AI88 5947|AI342534|AI653624|AW243936|T16586|R15989|AI289789|AI871636|AI718785|AW 148847 polyA = 2 polyA = 3 (SEQ ID NO: 29) >Hs.184601_mRNA_5 gi|4426639|gb/AF104032.1|AF104032 Homo sapiens polyA = 2 (SEQ ID NO: 30) >Hs.351972_singlet1 AA865917 polyA = 2 polyA = 3 (SEQ ID NO: 31) >Hs.5366_mRNA_2 gi|15277845|gb|BC012926.1|BC012926 Homo sapiens clone MGC: 16817 IMAGE: 3853503 polyA = 3 >Hs.18140_contig1 (SEQ ID NO: 32) AI685931|AA410954|T97707|AA706873|AI911572|AW614616|AA548520|AW027764|BF511 251|AI914294|AW151688 polyA = 1 polyA = 1 (SEQ ID NO: 33) >Hs.133196_contig2 BF224381|BE467992|AW137689|AI695045|AW207361|BF445141|AA405473 polyA = 2 WARN polyA = 3 (SEQ ID NO: 34) >Hs.63325_mRNA_5 gi|15451939|ref/NM_019894.1| Homo sapiens transmembrane protease, serine 4 (TMPRSS4), mRNA polyA = 3 (SEQ ID NO: 35) >Hs.250692_mRNA_2 gi|184223|gb|M95585.1|HUMHLF Human hepatic leukemia factor (HLF) mRNA, complete cds polyA = 3 (SEQ ID NO: 36) >Hs.250726_singlet4 AW298545 polyA = 2 polyA = 3 (SEQ ID NO: 37) >Hs.79217_mRNA_2 gi|16306657|gb|BC001504.1|BC001504 Homo sapiens clone MGC: 2273 IMAGE: 3505512 polyA = 3 (SEQ ID NO: 38) >Hs.47986_mRNA_1 gi|13279253|gb|BC004331.1|BC004331 Homo sapiens clone MGC: 10940 IMAGE: 3630835 polyA = 3 (SEQ ID NO: 39) >Hs.94367_mRNA_1 gi|10440200|dbj|AK027147.1|AK027147 Homo sapiens cDNA: FLJ23494 fis, clone LNG01885 polyA = 3 >Hs.49215_contig1 (SEQ ID NO: 40) BI493248|N66529|AA452255|BI492877|AW196683|AI963900|BF478125|AI421654|BE466 675 polyA = 1 polyA = 1 >Hs.281587_contig2 (SEQ ID NO: 41) R61469|R15891|AA007214|R61471|AI014624|N69765|AW592075|H09780|AA709038|AI33 5898|AI559229|F09750|R49594|H11055|T72573|AA935558|AA988654|AA826438|AI0024 31|AI299721 polyA = 1 polyA = 2 (SEQ ID NO: 42) >Hs.79378_mRNA_1 gi|16306528|ref|NM_003914.2| Homo sapiens cyclin A1 (CCNA1), mRNA polyA = 3 (SEQ ID NO: 43) >Hs.156469_contig2 AI341378|AI670817|AI701687|AI3In set 22|AW235883|AI948598|AA446356 polyA = 2 polyA = 3 (SEQ ID NO: 44) >Hs.6631_mRNA_1 gi|7020430|dbj|AK000380.1|AK000380 Homo sapiens cDNA FLJ20373 fis, clone HEP19740 polyA = 3 (SEQ ID NO: 45) >Hs.155977_contig1 AI309080|AI313045 polyA = 1 WARN polyA = 1 (SEQ ID NO: 46) >Hs.95197_mRNA_4 gi|5817138|emb|AL110274.1|HSM800829 Homo sapiens mRNA; cDNA DKFZp564I0272 (from clone DKFZp564I0272) polyA = 3 (SEQ ID NO: 47) >Hs.48956_contig1 N64339|AI569513|AI694073 polyA = 1 polyA = 1 (SEQ ID NO: 48) >Hs.118825_mRNA_10 gi|1495484|emb|X96757.1|HSSAPKK3 H.sapiens mRNA for MAP kinase kinase polyA = 3 >Hs.135118_contig3 (SEQ ID NO: 49) AI683181|AI082848|AW770198|AI333188|AI873435|AW169942|AI806302|AW340718|BF1 96955|AA909720 polyA = 1 polyA = 2 (SEQ ID NO: 50) >Hs.171857_mRNA_1 gi|13161080|gb|AF332224.1|AF332224 Homo sapiens testis protein mRNA, partial cds polyA = 3 (SEQ ID NO: 51) >Hs.18910_mRNA_3 gi|12804464|gb|BC001639.1|BC001639 Homo sapiens clone MGC: 1944 IMAGE: 2959372 polyA = 3 (SEQ ID NO: 52) >Hs.194774_mRNA_1 gi|16306633|gb|BC001492.1|BC001492 Homo sapiens clone MGC: 1774 IMAGE: 3510004 polyA = 3 (SEQ ID NO: 53) >Hs.127428_mRNA_2 gi|16306818|gb|BC006537.1|BC006537 Homo sapiens clone MGC: 1934 IMAGE: 2987903 polyA = 3 >Hs.126852_contig1 (SEQ ID NO: 54) AI802118|BF197404|BF224434|AA931964|AW236083|AI253119|AW614335|AI671372|AI7 93240|AW006851|AI953604|AI640505|AI633982|AW195809|AI493069|AW058576|AW2936 22 polyA = 2 polyA = 3 (SEQ ID NO: 55) >Hs.28149_mRNA_1 gi|14714936|gb|BC010626.1|BC010626 Homo sapiens clone MGC: 17687 IMAGE: 3865868 polyA = 3 (SEQ ID NO: 56) >Hs.35453_mRNA_3 gi|7018494|emb|AL157475.1|HSM802461 Homo sapiens mRNA; cDNA DKFZp761G151 (from clone DKFZp761G151); partial cds polyA = 3 (SEQ ID NO: 57) >Hs.180570_contig1 R08175|AA707224|AA699986|R11209|W89099|T98002|AA494546 polyA = 2 polyA = 3 (SEQ ID NO: 58) >Hs.196270_mRNA_1 gi|11545416|gb|AF283645.1|AF283645 Homo sapiens chromosome 8 map 8q21 polyA = 3 (SEQ ID NO: 59) >Hs.9030_mRNA_3 gi|12652600|gb|BC000045.1|BC000045 Homo sapiens clone MGC: 2032 IMAGE: 3504527 polyA = 3 (SEQ ID NO: 60) >Hs.1282_mRNA_3 gi|4559405|ref|NM_000065.1| Homo sapiens complement component 6 (C6), mRNA polyA = 1 (SEQ ID NO: 61) >Hs.268562_mRNA_2 gi|15341874|gb|BC013117.1|BC013117 Homo sapiens clone MGC: 8711 IMAGE: 3882749 polyA = 3 (SEQ ID NO: 62) >Hs.151301_mRNA_3 gi|16041747|gb|BC015754.1|BC015754 Homo sapiens clone MGC: 23085 IMAGE: 4862492 polyA = 3 (SEQ ID NO: 63) >Hs.111_contig1 AA946776|AW242338|H24274|AI078616 polyA = 1 polyA = 2 (SEQ ID NO: 64) >Hs.150753_contig1 AI123582|AI288234 polyA = 0 polyA = 0 (SEQ ID NO: 65) >Hs.82109_mRNA_1 gi|14250611|gb|BC008765.1|BC008765 Homo sapiens clone MGC: 1622 IMAGE: 3347793 polyA = 3 (SEQ ID NO: 66) >Hs.44276_mRNA_2 gi|12654896|gb|BC001293.1|BC001293 Homo sapiens clone MGC: 5259 IMAGE: 3458115 polyA = 3 (SEQ ID NO: 67) >Hs.2142_mRNA_4 gi|13325274|gb|BC004453.1|BC004453 Homo sapiens clone MGC: 4303 IMAGE: 2819400 polyA = 3 (SEQ ID NO: 68) >Hs.180908_contig1 AA846824|AW611680|AA846182|AA846342|AA846360 polyA = 2 polyA = 3 (SEQ ID NO: 69) >Hs.89436_mRNA_1 gi|16507959|ref|NM_004063.2| Homo sapiens cadherin 17, LI cadherin (liver-intestine) (CDH17), mRNA polyA = 1 (SEQ ID NO: 70) >Hs.151544_mRNA_8 gi|3153107|emb|AL023657.1|HSDSHP Homo sapiens SH2D1A cDNA, formerly known as DSHP polyA = 3 >Hs.1657_contig4 (SEQ ID NO: 71) AW473119|AA164586|AI540656|AI758480|AI810941|AI978964|AI675862|AI784397|AW5 91562|AW514102|AI888116|AI983175|AI634735|AI669577|AI202659|AI910598|AI9613 52|AI565481|AI886254|AI538838|AA291749|AW571455|AI370308|AI274727|AW473925| AW514787|AI273871|AW470552|AI524356|AI888281|AW089672|AI952766|AW440601|AI6 54044|AW438839|AI972926 polyA = 2 polyA = 3 (SEQ ID NO: 72) >Hs.35984_mRNA_1 gi|6049161|gb|AF133587.1|AF133587 Homo sapiens chromosome 22 map 22q11.2 polyA = 3 (SEQ ID NO: 73) >Hs.334534_mRNA_2 gi|17389403|gb|BC017742.1|BC017742 Homo sapiens, clone IMAGE: 4391536, mRNA polyA = 3 (SEQ ID NO: 74) >Hs.60162_mRNA_1 gi|10437644|dbj|AK025181.1|AK025181 Homo sapiens cDNA: FLJ21528 fis, clone COL05977 polyA = 3

As would be understood by the skilled person, detection of expression of any of the above identified sequences, as well as sequences of the set of 90 below, or the sequences provided in the attached Sequence Listing may be performed by the detection of expression of any appropriate portion or fragment of these sequences. Preferably, the portions are sufficiently large to contain unique sequences relative to other sequences expressed in a cell containing sample. Moreover, the skilled person would recognize that the disclosed sequences represent one strand of a double stranded molecule and that either strand may be detected as an indicator of expression of the disclosed sequences. This follows because the disclosed sequences are expressed as RNA molecules in cells which are preferably converted to cDNA molecules for ease of manipulation and detection. The resultant cDNA molecules may have the sequences of the expressed RNA as well as those of the complementary strand thereto. Thus either the RNA sequence strand or the complementary strand may be detected. Of course is it also possible to detect the expressed RNA without conversion to cDNA.

In some embodiments of the invention, the expression levels of gene sequences is measured by detection of expressed sequences in a cell containing sample as hybridizing to the following oligonucleotides, which correspond to the above sequences as indicated by the accession numbers provided.

>AF133587 (SEQ ID NO: 75) CCCGGATCGCCATCAGTGTCATCGAGTTCAAACCCTGAGCCCTTCATTCACCTCTGTGAG >BC017742 (SEQ ID NO: 76) TGCCCTTGCTCTGTGTCATCTCAGTCATTTGACTTAGAAAGTGCCCTTCAAAAGGACCCT >BF437393 (SEQ ID NO: 77) GGAGGGAGGGCTAATTATATATTTTGTTGTTCCTCTATACTTTGTTCTGTTGTCTGCGCC >AI620495 (SEQ ID NO: 78) CAGTTTGGATTGTATAATAACGCCAAGCCCAGTTGTAGTCGTTTGAGTGCAGTAATGAAA >AK000380 (SEQ ID NO: 79) AAATCAGAGTAACCCTTTCTGTATTGAGTGCAGTGTTTTTTACTCTTTTCTCATGCACAT >BC009237 (SEQ ID NO: 80) TGCCTGGCACAAAGAAGGAAGAATATAAATGATAGTTCGACTCGTCTGTGGAAGAACTTA >BC008765 (SEQ ID NO: 81) AGTCTTTTGCTTTTGGCAAAACTCTACTTAATCCAATGGGTTTTTCCCTGTACAGTAGAT >BC001504 (SEQ ID NO: 82) GGTTACTGTGGGTGGAATAGTGGAGGCCTTCAACTGATTAGACAAGGCCCGCCCACATCT >NM_019894 (SEQ ID NO: 83) TAAAATGCACTGCCCTACTGTTGGTATGACTACCGTTACCTACTGTTGTCATTGTTATTA >BF224381 (SEQ ID NO: 84) TTCTCTTTTGGGGGCAAACACTATGTCCTTTTCTTTTTCTAGATACAGTTAATTCCTGGA >AL157475 (SEQ ID NO: 85) AAGACCCACACCCTGTAGCAATACCAAGTGCTATTACATAATCAATGGACGATTTATACT >AY033998 (SEQ ID NO: 86) AGTGTTGCAAGTTTCCTTTAAAACCAACAAAGCCCACAAGTCCTGAATTTCCCATTCTTA >H07885 (SEQ ID NO: 87) GTCACTGTCATAGCAGCTGTGATTTCACAAGGAAGGGTGCTGCAGGGGGACCTGGTTGAT >NM_004496 (SEQ ID NO: 88) TTTCATCCAGTGTTATGCACTTTCCACAGTTGGTGTTAGTATAGCCAGAGGGTTTCATTA >AA846824 (SEQ ID NO: 89) GGGAAGTAGGGATTATTCGTTTAAATTCAATCGCGAGCACCAAGTCGGACTGGCCGGGGA >BC017586 (SEQ ID NO: 90) GGGACCAGGCCCTGGGACAGCCATGTGGCTCCAAATGACTAAATGTCAGCTCAAAAACCA >AA456140 (SEQ ID NO: 91) TCCGTTTATGGAGGCAATTCCATATCCTTTCTTGAACGCACATTCAGCTTACCCCAGAGA >NM_002283 (SEQ ID NO: 92) AGAGTTAAGCCACTTCCTGGGTCTCCTTCTTATGACTGTCTATGGGTGCATTGCCTTCTG >AL023657 (SEQ ID NO: 93) GTGGCCTGAGTAATGCATTATGGGTGGTTTACCATTTCTTGAGGTAAAAGCATCACATGA >BC001639 (SEQ ID NO: 94) ACACATGCATGTGTCTGTGTATGTGTGAATGTGAGAGAGACACAGCCCTCCTTTCAGAAG >BC015754 (SEQ ID NO: 95) TCTGTAACTGCACAACCCTGGGGTTTGCTGCAGAGCTATTTCTTTCCATGTAAAGTAGTG >AF332224 (SEQ ID NO: 96) AAACACTCTTTCCGACTCCAGAGGAGAAGCTGGCAGCTCTCTGTAAGAAATATGCTGATC >BC001270 (SEQ ID NO: 97) GCTTCCTCTATCGCCCAATGCAAAATCGATGAAATGGGGAGTTCTCTGGGCCAGGCCACA >AI147926 (SEQ ID NO: 98) GTAGAATCCTCTGTTCATAATGAACAAGATGAACCAATGTGGATTAGAAAGAAGTCCGAG >AW298545 (SEQ ID NO: 99) CTGTTTTAAAACTGAATGGCACGAAATTGTTTTCCTCAACTCGGAGATTCCTGTATGGAG >AI802118 (SEQ ID NO: 100) AATAAATAGTAGCTCTGCTGATGATGACGTTGATAACCAAACTGTTCTGTGGTCTTAAGT >AI683181 (SEQ ID NO: 101) CAAACAGCCCGGTCTTGATGCAGGAGAGTCTGGAAAAGGAAGAAAATGGTTTCAGTTTCA >M95585 (SEQ ID NO: 102) AACATGGACCATCCAAATTTATGGCCGTATCAAATGGTAGCTGAAAAAACTATATTTGAG >AK027147 (SEQ ID NO: 103) TTGTAATCATGCCAATTCCAGATCAATAACTGCATGTCTGTTCTTTGGTAGAAATAGCTT >AW291189 (SEQ ID NO: 104) AAAGATTATTAACCCAAATCACCTTTCTTGCTTACTCCAGATGCCTCAGCCTCTGATATA >AI632869 (SEQ ID NO: 105) GACTTCCTTTAGGATCTCAGGCTTCTGCAGTTCTCATGACTCCTACTTTTCATCCTAGTC >BC006537 (SEQ ID NO: 106) CTGTATATTTTGCAATAGTTACCTCAAGGCCTACTGACCAAATTGTTGTGTTGAGATGAT >R61469 (SEQ ID NO: 107) TGTTCAAACAGACTTTAACCTCTGCATCATACTTAACCCTGCGACATGCGTACAGTATGC >BC009084 (SEQ ID NO: 108) TGAGTCATATACATTTACTGACCACTGTTGCTTGTTGCTCACTGTGCTGCTTTTCCATGA >N64339 (SEQ ID NO: 109) CTGAAATGTGGATGTGATTGCCTCAATAAAGCTCGTCCCCATTGCTTAAGCCTTCAAAAA >AI200660 (SEQ ID NO: 110) ATCAAGAAAACCTAATCTTCTGACTCCCAGGCCAGGATGTTTTATTTCTCACATCATGTC >AK054605 (SEQ ID NO: 111) TTCATTTCCAAACATCATCTTTAAGACTCCAAGGATTTTTCCAGGCACAGTGGCTCATAC >NM_006115 (SEQ ID NO: 112) AGTTAGAAATAGAATCTGAATTTCTAAAGGGAGATTCTGGCTTGGGAAGTACATGTAGGA >X96757 (SEQ ID NO: 113) CAATTTTCTTTTTACTCCCCCTCTTAAGGGGGCCTTGGAATCTATAGTATAGAATGAACT >AI804745 (SEQ ID NO: 114) GGGTGGAGTTTCAGTGAGAATAAACGTGTCTGCCTTTGTGTGTGTGTATATATACAGAGA >AJ000388 (SEQ ID NO: 115) CTCGCTCATTTTTTACCATGTTTTCCAGTCTGTTTAACTTCTGCAGTGCCTTCACTACAC >BC008764 (SEQ ID NO: 116) CTTTGGGCCGAGCACTGAATGTCTTGTACTTTAAAAAAATGTTTCTGAGACCTCTTTCTA >AI309080 (SEQ ID NO: 117) CTGGACCCTTGGAGCAGTGTTGTGTGAACTTGCCTAGAACTCTGCCTTCTCCGTTGTCAA >AA865917 (SEQ ID NO: 118) CCACCTCCTTCGACCTCCACTGCGCCCCACCTCCCTGCCTGTGTGTGTTATTTCAAAGGA >AA946776 (SEQ ID NO: 119) TCTGGCTGGTGGCCTGCGCGAGGGTGCAGTCTTACTTAAAAGACTTTCAGTTAATTCTCA >AF104032 (SEQ ID NO: 120) AGATGCTGTCGGCACCATGTTTATTTATTTCCAGTGGTCATGCTCAGCCTTGCTGCTCTG >AW194680 (SEQ ID NO: 121) TCCTTCCTCTTCGGTGAATGCAGGTTATTTAAACTTTGGGAAATGTACTTTTAGTCTGTC >BC001293 (SEQ ID NO: 122) GTCCTGTCCCTGTCTGGGAGTTGTGTTATTTAAAGATATTCTGTATGTTGTATCTTTTGC >BE962007 (SEQ ID NO: 123) ATTATATTTCAGGTGTCCTGAACAGGTCACTAGACTCTACATTGGGCAGCCTTTAAATAT >BI493248 (SEQ ID NO: 124) AGGAATGGTACTACCGTTCCAGATTTTCTGTAATTGCTTCTGCAAAGTAATAGGCTTCTT >AF283645 (SEQ ID NO: 125) CTGTACCCAAAGGATGCCAGAATACTAGTATTTTTATTTATCGTAAACATCCACGAGTGC >AI669760 (SEQ ID NO: 126) ATTGCCCCCCTAACCAATCATGCAAACTTTTCCCCCCCTGGGGTAATTCACCAGTTAAAA >BC001492 (SEQ ID NO: 127)) CCCACAGTATTTAATGCCCTGTCAGTCCCTTCTAGTCTGACTCAATGGTAACTTGCTGTA >BC004453 (SEQ ID NO: 128) AAAACCAACTCTCTACTACACAGGCCTGATAACTCTGTACGAGGCTTCTCTAACCCCTAG >BC010626 (SEQ ID NO: 129) CTCAGACTGGGCTCCACACTCTTGGGCTTCAGTCTGCCCATCTGCTGAATGGAGACAGCA >BC013117 (SEQ ID NO: 130) CCTAATGGGGATTCCTCTGGTTGTTCACTGCCAAAACTGTGGCATTTTCATTACAGGAGA >BC011949 (SEQ ID NO: 131) CACTCACAATTGTTGACTAAAATGCTGCCTTTAAAACATAGGAAAGTAGAATGGTTGAGT >AW083022 (SEQ ID NO: 132) CTTTGAAGGGCTGCTGCACATTGTTGAATCCATCGACCTTTAGCTGCAATGGGATCTCTA >R08175 (SEQ ID NO: 133) TGCCTCATCGATATTATAGGGGTCCATCACAACCCAACTGTGTGGCCGGATCCTGAGTCT >NM_000065 (SEQ ID NO: 134) AAAACAGACAAAAGCCTTTGCCTTCATGAAGCATACATTCATTCAGGGGTAGACACACAA >AK025181 (SEQ ID NO: 135) TAACAAACAAAGGCAGTAGCTCATCACTTGGGTAGCAGGTACCCATTTTAGGACCCTACA >NM_003914 (SEQ ID NO: 136) ATATCAGAAGTGCCAATAATCGTCATAGGCTTCTGCACGTTGGATCAACTAATGTTGTTT >AI123582 (SEQ ID NO: 137) ATCATAGCCCAACCATGTGAGAAGAAGGAGAAGGCCCCCCTTTCTTCATTAATCTGAAAA >BC004331 (SEQ ID NO: 138) GCAGACCATTCTATCATACCTGGCAGGGCTTCTGTTTTATTTTGTAGGCTGGATGCTACC >AI341378 (SEQ ID NO: 139) ACTACAAGCCTCTTGTTTTTCACCAAAACCCTACATCTCAGGCTTACTAATTTTTGTGAT >NM_004063 (SEQ ID NO: 140) GCCATGCATACATGCTGCGCATGTTTTCTTCATTCGTATGTTAGTAAAGTTTTGGTTATT >BC012926 (SEQ ID NO: 141) CACCTATTTATTTTACCTCTTTCCCAAACCTGGAGCATTTATGCCTAGGCTTGTCAAGAA >AL110274 (SEQ ID NO: 142) GTGGACATAGCCACTAACCAACTAGTTACCTTTGGACTGCAACAAAAAATGTGAAAATGA >AW473119 (SEQ ID NO: 143) ACTTGTAAACCTCTTTTGCACTTTGAAAAAGAATCCAGCGGGATGCTCGAGCACCTGTAA >AI685931 (SEQ ID NO: 144) AATTCTCTATAAACGGTTCACCAGCAAACCACCAATACATTCCATTGTTTGCCTAGAGAG >BF592799 (SEQ ID NO: 145) AATGGCCCATGCATGCTGTTTGCAGCAGTCAATTGAGTTGAATTAGAATTCCAACCATAC >BC000045 (SEQ ID NO: 146) GAGCTCAGTACTTGCCCTGTGAAAATCCCAGAAGCCCCCGCTGTCAATGTTCCCCATCCA >BC015582 (SEQ ID NO: 147) ATGAAGCGGAATTAGGCTCCCGAGCTAAGGGACTCGCCTAGGGTCTCACAGTGAGTAGGA >M60502 (SEQ ID NO: 148) AGTGGCTATATCAACATCAGGGCTAGCACATCTTTCTCTATTATCCTTCTATTGGAATTC

The invention also provides a second group of 90 gene sequences from which 50 or more may be used in the practice of the invention. The 50 to 90 gene sequences may be used along with the determination of expression levels of additional sequences so long as the expression levels of gene sequences from the set of 90 are used in classifying. A non-limiting example of such embodiments of the invention is where the expression of the 90 gene sequences, or at least 50 (or 50 to about 90) members thereof, is measured along with the expression levels of a plurality of other sequences, such as by use of a microarray based platform used to perform the invention. Where those other expression levels are not used in classification, they may be considered the results of “excess” transcribed sequences and not critical to the practice of the invention. Alternatively, and where those other expression levels are used in classification, they are within the scope of the invention, where the use of the above described sequences does not necessarily exclude the use of expression levels of additional sequences.

38 members of the set of 90 are included in the first set of 74 described above. The accession numbers of these members in common between the two sets are AA456140, AA846824, AA946776, AF332224, AI620495, AI632869, AI802118, AI804745, AJ000388, AK025181, AK027147, AL157475, AW194680, AW291189, AW298545, AW473119, BC000045, BC001293, BC001504, BC004453, BC006537, BC008765, BC009084, BC011949, BC012926, BC013117, BC015754, BE962007, BF224381, BF437393, BI493248, M60502, NM_(—)000065, NM_(—)003914, NM_(—)004063, NM_(—)004496, NM_(—)006115, and R61469. mRNA sequences corresponding to members of the set of 90 that are not present in the set of 74 gene sequences are also provided in the Sequence Listing and identified as SEQ ID NOS: 149-200. The listing of identifying information for these 52 unique members by accession numbers, as well as corresponding oligonucleotide sequences which may be used in the practice of the invention, is provided by the following.

>R15881 (SEQ ID NO: 201) ACTTCTGGTGATGATAAAAATGGTTTTATCACCCAGATGTGAAAGAAGCTGCCTGTTTAC >AI041545 (SEQ ID NO: 202) GTGGTTCTGTAAAAACGCAGAGGAAAAGAGCCAGAAGGTTTCTGTTTAATGCATCTTGCC >NM_024423 (SEQ ID NO: 203) TTTATAAGGAAGCAGCTGTCTAAAATGCAGTGGGGTTTGTTTTGCAATGTTTTAAACAGA >AB038160 (SEQ ID NO: 204) CTTATGAAGCTGGCCGGGCCACTCACGTTCAATGGTACATCTGGGTCTCTATGTGGTTCT >AK026790 (SEQ ID NO: 205) GTGAGCCAGCATTTCCCATAGCTAACCCTATTCTCTTAGTCTTTCAAAATGTAGAATGGG >BC012727 (SEQ ID NO: 206) CTTTACACCTGATAAAATATTTTGCGAAGAGAGGTGTTCTTTTTCCTTACTGGTGCTGAA >BC016451 (SEQ ID NO: 207) GCATACATCTCATCCACAGGGGAAGATAAAGATGGTCACACAAACAGTTTCCATAAAGAT >H09748 (SEQ ID NO: 208) TGAGTTCAGCATGTGTCTGTCCATTTCATTTGTACGCTTGTTCAAAACCAAGTTTGTTCT >NM_006142 (SEQ ID NO: 209) AAGACCGAGACTGAGGGAAAGCATGTCTGCTGGGTGTGACCATGTTTCCTCTCAATAAAG >AF191770 (SEQ ID NO: 210) GGCATCTGGCCCCTGGTAGCCAGCTCTCCAGAATTACTTGTAGGTAATTCCTCTCTTCAT >NM_006378 (SEQ ID NO: 211) TGGATGTTTGTGCGCGTGTGTGGACAGTCTTATCTTCCAGCATGATAGGATTTGACCATT >BC006819 (SEQ ID NO: 212) TCCTGGCAGAGCCATGGTCCCAGGCTTCCCAAAAGTGTTTGTGGCAATTATTCCCCTAGG >X79676 (SEQ ID NO: 213) TTTGATGATAGCAGACATTGTTACAAGGACATGGTGAGTCTATTTTTAATGCACCAATCT >BC006811 (SEQ ID NO: 214) TTCTTCCAGTTGCACTATTCTGAGGGAAAATCTGACACCTAAGAAATTTACTGTGAAAAA >NM_000198 (SEQ ID NO: 215) GAACAATTGTGGTCTCTCTTAACTTGAGGTTCTCTTTTGACTAATAGAGCTCCATTTCCC >AF301598 (SEQ ID NO: 216) GTTAAGTGTGGCCAAGCGCACGGCGGCAAGTTTTCAAGCACTGAGTTTCTATTCCAAGAT >NM_002847 (SEQ ID NO: 217) CGGCCTACTGAGCGGACAGAATGATGCCAAAATATTGCTTATGTCTCTACATGGTATTGT >NM_004062 (SEQ ID NO: 218) CAGGGTGTTTGCCCAATAATAAAGCCCCAGAGAACTGGGCTGGGCCCTATGGGATTGGTA >AW118445 (SEQ ID NO: 219) TGTACAGTTTGGTTGTTGCTGTAAATATGGTAGCGTTTTGTTGTTGTTGTTTTTTCATGC >BC002551 (SEQ ID NO: 220) TACCAAACTGGGACTCACAGCTTTATTGGGCTTTCTTTGTGTCTTGTGTGTTTCTTTTAT >AA765597 (SEQ ID NO: 221) CATTGAGGTTTGGATGGTGGCAGGTAAAACAGAAAGGCAAGATGTCATCTGACATTAGGC >AL137761 (SEQ ID NO: 222) AGTTCAGCACTGTGGTTATCATTGGTGATGCCAGAAAACATTAGTAGACTTAGACAATTG >X78202 (SEQ ID NO: 223) TAAAATTTCTTGATTGTGACTATOTGGTCATATGCCCGTGTTTGTCACTTACAAAAATGT >AK025615 (SEQ ID NO: 224) AGCCATCTGGTGTGAAGAACTCTATATTTGTATGTTGAGAGGGCATGGAATAATTGTATT >BC001665 (SEQ ID NO: 225) CTTATTGTCACTGGTTAAGAACTTGGCGAGATTGAAGGGCTTTTGTTATTGTTGTTGGAT >AI985118 (SEQ ID NO: 226) CTTTCTAGTGAGCTAACCGTAACAGAGAGCCTACAGGATACACGTGAGATAATGTCACGT >AL039118 (SEQ ID NO: 227) TTGTCTTAAAATTTCTTGATTGTGATACTGTGGTCATATGCCCGTGTTTGTCACTTACAA >AA782845 (SEQ ID NO: 228) CCTGGGGGAAAGGGGCATTCATGACCTGAACTTTTTAGCAAATTATTATTCTCAGTTTCC >BC016340 (SEQ ID NO: 229) TTCATTAACAGTACTAAGTGGAAGGGATCTGCAGATTCCAAATTGGAATAAGCTCTATCA >AA745593 (SEQ ID NO: 230) CCAATGCAGAAGAGTATTAAGAAAGATGCTCAAGTCCCATGGCACAGAGCAAGGCGGGCA >NM_004967 (SEQ ID NO: 231) CAAGGCTACGATGGCTATGATGGTCAGAATTACTACCACCACCAGTGAAGCTCCAGCCTG >BF510316 (SEQ ID NO: 232) AGCTCACAGCTGGACAGGTGTTGTATATAGAGTGGAATCTCTTGGATGCAGCTTCAAGAA >AA993639 (SEQ ID NO: 233) TCCAAAGTAGAAAGGGTTCTTTTAGAAAACTTGAAGAATGTGCCTCCTCTTAGCATCTGT >AV656862 (SEQ ID NO: 234) GATGCATTTTTCAGTCCCTTTTCAGAGCAAATGCTTTTGCAATGGTAGTAATGTTTAGTT >X69699 (SEQ ID NO: 235) CCTGTGGGGCTTCTCTCCTTGATGCTTCTTTCTTTTTTTAAAGACAACCTGCCATTACCA >BC013282 (SEQ ID NO: 236) TTGCACTAAGTCATGCTGTTTCCTCAAAGAAGCTTTGTTTTTTGTTAACGTATTACTCAG >AI457360 (SEQ ID NO: 237) CTGGATCCCAGGCCCTGGCACCCCTCAGGAAATACAAGAAAAAGAATATTCACATCTGTT >AW445220 (SEQ ID NO: 238) TTAGAGGGGCCACCTATCAACTCATCAGTGTTCAAAGAATATGCTGGGAGCATGGGTGAG >AF038191 (SEQ ID NO: 239) GGCCCATTTATGTCCCTCATGTCTCTAGATTTTCTCGTCACCCAGCCTCAAAAATATATG >X05615 (SEQ ID NO: 240) TCCCCAAAAACCTCACCCGAGGCTGCCCACTATGGTCATCTTTTTCTCTAAAATAGTTAC >BC005364 (SEQ ID NO: 241) GAAATTCCTCACACCTTGCACCTTCCCTACTTTTCTGAATTGCTATGACTACTCCTTGTT >AK025701 (SEQ ID NO: 242) TGTCTGTCCACCACGAGATGGGAGGAGGAGAAAAAGCGGTACGATGCCTTCCTGACCTCA >BF446419 (SEQ ID NO: 243) GTCTTATCTCTCAGGGGGGGTTTAAGTGCCGTTTGCAATAATGTCGTCTTATTTATTTAG >AK025470 (SEQ ID NO: 244) CCGAGTAGTATGGGTCTCTGTGTGAGAAACCAGGAGATATTTTCATCTTGTTCGGAAATA >BE552004 (SEQ ID NO: 245) TTGTGCAAAAGTCCCACAACCTTTCTGGATTGATAGTTTGTGGTGAAATAAACAATTTTA >H05388 (SEQ ID NO: 246) TCCAGTATTCTGCAGGGCCAGTCAGTTGTACAGAAGTTGGAATATTCTGTTCCAGAATTA >NM_033229 (SEQ ID NO: 247) GTCTCGAACAGCGGTTGTTTTTACTTTATTTATCTTAGGCCCTCAGCTCCCTGACGTCCT >BC010437 (SEQ ID NO: 248) AGTGAATCTTTTCCTCTTGGTAGCATCAACACTGGGGATAAATCAGAACCATTCTGTGGA >AI952953 (SEQ ID NO: 249) TGAGAGCCCAGAACAAGAAGGAGCAGAAGGGCACTTTGACCTTCATTATTATGAAAATCA >R45389 (SEQ ID NO: 250) GGAAGAACTGATGCTTGCTGCTAACTAAAGTTTTGGATGTATCGATTTAGAGAACCAATT >NM_001337 (SEQ ID NO: 251) GAATGAGAGAATAAGTCATGTTCCTTCAAGATCATGTACCCCAATTTACTTGCCATTACT >AI499593 (SEQ ID NO: 252) TACGGAAAGGAAACAGGTTATACTCTTAGATTTAAAAAGTGAAAGAAACTGCAGGCGCCT

In some embodiments of the invention, the expression levels of gene sequences is measured by detection of expressed sequences in a cell containing sample as hybridizing to the above oligonucleotides, which correspond to sequences in the Sequence Listing as indicated by the accession numbers provided.

In additional embodiments, the invention provides for use of any number of the gene sequences of the set of 74 or the set of 90 in the methods of the invention. Thus anywhere from 1 to all of the 50 or more gene sequences used in the invention may be from either or both of the above sets. So from one, two, three, four, five, six, seven, eight, nine, ten, or more of the 50 or more sequences may be from the set of 74 or the set of 90.

As used herein, a “tumor sample” or “tumor containing sample” or “tumor cell containing sample” or variations thereof, refer to cell containing samples of tissue or fluid isolated from an individual suspected of being afflicted with, or at risk of developing, cancer. The samples may contain tumor cells which may be isolated by known methods or other appropriate methods as deemed desirable by the skilled practitioner. These include, but are not limited to, microdissection, laser capture microdissection (LCM), or laser microdissection (LMD) before use in the instant invention. Alternatively, undissected cells within a “section” of tissue may be used. Non-limiting examples of such samples include primary isolates (in contrast to cultured cells) and may be collected by any non-invasive or minimally invasive means, including, but not limited to, ductal lavage, fine needle aspiration, needle biopsy, the devices and methods described in U.S. Pat. No. 6,328,709, or any other suitable means recognized in the art. Alternatively, the sample may be collected by an invasive method, including, but not limited to, surgical biopsy.

The detection and measurement of transcribed sequences may be accomplished by a variety of means known in the art or as deemed appropriate by the skilled practitioner. Essentially, any assay method may be used as long as the assay reflects, quantitatively or qualitatively, expression of the transcribed sequence being detected.

The ability to classify tumor samples is provided by the recognition of the relevance of the level of expression of the gene sequences (whether randomly selected or specified) and not by the form of the assay used to determine the actual level of expression. An assay of the invention may utilize any identifying feature of a individual gene sequence as disclosed herein as long as the assay reflects, quantitatively or qualitatively, expression of the gene in the “transcriptome” (the transcribed fraction of genes in a genome) or the “proteome” (the translated fraction of expressed genes in a genome). Additional assays include those based on the detection of polypeptide fragments of the relevant member or members of the proteome. Non-limiting examples of the latter include detection of proteolytic fragments found in a biological fluid, such as blood or serum. Identifying features include, but are not limited to, unique nucleic acid sequences used to encode (DNA), or express (RNA), said gene or epitopes specific to, or activities of, a protein encoded by a gene sequence.

Additional means include detection of nucleic acid amplification as indicative of increased expression levels and nucleic acid inactivation, deletion, or methylation, as indicative of decreased expression levels. Stated differently, the invention may be practiced by assaying one or more aspect of the DNA template(s) underlying the expression of each gene sequence, of the RNA used as an intermediate to express the sequence, or of the proteinaceous product expressed by the sequence, as well as proteolytic fragments of such products. As such, the detection of the presence of, amount of, stability of, or degradation (including rate) of, such DNA, RNA and proteinaceous molecules may be used in the practice of the invention.

In some embodiments, all or part of a gene sequence may be amplified and detected by methods such as the polymerase chain reaction (PCR) and variations thereof, such as, but not limited to, quantitative PCR (Q-PCR), reverse transcription PCR (RT-PCR), and real-time PCR (including as a means of measuring the initial amounts of mRNA copies for each sequence in a sample), optionally real-time RT-PCR or real-time Q-PCR. Such methods would utilize one or two primers that are complementary to portions of a gene sequence, where the primers are used to prime nucleic acid synthesis. The newly synthesized nucleic acids are optionally labeled and may be detected directly or by hybridization to a polynucleotide of the invention. The newly synthesized nucleic acids may be contacted with polynucleotides (containing gene sequences) of the invention under conditions which allow for their hybridization. Additional methods to detect the expression of expressed nucleic acids include RNAse protection assays, including liquid phase hybridizations, and in situ hybridization of cells.

Alternatively, the expression of gene sequences in FFPE samples may be detected as disclosed in U.S. applications 60/504,087, filed Sep. 19, 2003, 10/727,100, filed Dec. 2, 2003, and 10/773,761, filed Feb. 6, 2004 (all three of which are hereby incorporated by reference as if fully set forth). Briefly, the expression of all or part of an expressed gene sequence or transcript may be detected by use of hybridization mediated detection (such as, but not limited to, microarray, bead, or particle based technology) or quantitative PCR mediated detection (such as, but not limited to, real time PCR and reverse transcriptase PCR) as non-limiting examples. The expression of all or part of an expressed polypeptide may be detected by use of immunohistochemistry techniques or other antibody mediated detection (such as, but not limited to, use of labeled antibodies that bind specifically to at least part of the polypeptide relative to other polypeptides) as non-limiting examples. Additional means for analysis of gene expression are available, including detection of expression within an assay for global, or near global, gene expression in a sample (e.g. as part of a gene expression profiling analysis such as on a microarray). Non-limiting examples linear RNA amplification and those described in U.S. patent application Ser. No. 10/062,857 (filed on Oct. 25, 2001), as well as U.S. Provisional Patent Applications 60/298,847 (filed Jun. 15, 2001) and 60/257,801 (filed Dec. 22, 2000), all of which are hereby incorporated by reference in their entireties as if fully set forth.

In embodiments using a nucleic acid based assay to determine expression includes immobilization of one or more gene sequences on a solid support, including, but not limited to, a solid substrate as an array or to beads or bead based technology as known in the art. Alternatively, solution based expression assays known in the art may also be used. The immobilized gene sequence(s) may be in the form of polynucleotides that are unique or otherwise specific to the gene(s) such that the polynucleotides would be capable of hybridizing to the DNA or RNA of said gene(s). These polynucleotides may be the full length of the gene(s) or be short sequences of the genes (up to one nucleotide shorter than the full length sequence known in the art by deletion from the 5′ or 3′ end of the sequence) that are optionally minimally interrupted (such as by mismatches or inserted non-complementary basepairs) such that hybridization with a DNA or RNA corresponding to the genes is not affected. In some embodiments, the polynucleotides used are from the 3′ end of the gene, such as within about 350, about 300, about 250, about 200, about 150, about 100, or about 50 nucleotides from the polyadenylation signal or polyadenylation site of a gene or expressed sequence. Polynucleotides containing mutations relative to the sequences of the disclosed genes may also be used so long as the presence of the mutations still allows hybridization to produce a detectable signal. Thus the practice of the present invention is unaffected by the presence of minor mismatches between the disclosed sequences and those expressed by cells of a subject's sample. A non-limiting example of the existence of such mismatches are seen in cases of sequence polymorphisms between individuals of a species, such as individual human patients within Homo sapiens.

As will be appreciated by those skilled in the art, some gene sequences include 3′ poly A (or poly T on the complementary strand) stretches that do not contribute to the uniqueness of the disclosed sequences. The invention may thus be practiced with gene sequences lacking the 3′ poly A (or poly T) stretches. The uniqueness of the disclosed sequences refers to the portions or entireties of the sequences which are found only in nucleic acids, including unique sequences found at the 3′ untranslated portion thereof. Some unique sequences for the practice of the invention are those which contribute to the consensus sequences for the genes such that the unique sequences will be useful in detecting expression in a variety of individuals rather than being specific for a polymorphism present in some individuals. Alternatively, sequences unique to an individual or a subpopulation may be used. The unique sequences may be the lengths of polynucleotides of the invention as described herein.

In additional embodiments of the invention, polynucleotides having sequences present in the 3′ untranslated and/or non-coding regions of gene sequences are used to detect expression levels in cell containing samples of the invention. Such polynucleotides may optionally contain sequences found in the 3′ portions of the coding regions of gene sequences. Polynucleotides containing a combination of sequences from the coding and 3′ non-coding regions preferably have the sequences arranged contiguously, with no intervening heterologous sequence(s).

Alternatively, the invention may be practiced with polynucleotides having sequences present in the 5′ untranslated and/or non-coding regions of gene sequences to detect the level of expression in cells and samples of the invention. Such polynucleotides may optionally contain sequences found in the 5′ portions of the coding regions. Polynucleotides containing a combination of sequences from the coding and 5′ non-coding regions may have the sequences arranged contiguously, with no intervening heterologous sequence(s). The invention may also be practiced with sequences present in the coding regions of gene sequences.

The polynucleotides of some embodiments contain sequences from 3′ or 5′ untranslated and/or non-coding regions of at least about 16, at least about 18, at least about 20, at least about 22, at least about 24, at least about 26, at least about 28, at least about 30, at least about 32, at least about 34, at least about 36, at least about 38, at least about 40, at least about 42, at least about 44, or at least about 46 consecutive nucleotides. The term “about” as used in the previous sentence refers to an increase or decrease of 1 from the stated numerical value. Other embodiments use polynucleotides containing sequences of at least or about 50, at least or about 100, at least about or 150, at least or about 200, at least or about 250, at least or about 300, at least or about 350, or at least or about 400 consecutive nucleotides. The term “about” as used in the preceding sentence refers to an increase or decrease of 10% from the stated numerical value.

Sequences from the 3′ or 5′ end of gene coding regions as found in polynucleotides of the invention are of the same lengths as those described above, except that they would naturally be limited by the length of the coding region. The 3′ end of a coding region may include sequences up to the 3′ half of the coding region. Conversely, the 5′ end of a coding region may include sequences up the 5′ half of the coding region. Of course the above described sequences, or the coding regions and polynucleotides containing portions thereof, may be used in their entireties.

In another embodiment of the invention, polynucleotides containing deletions of nucleotides from the 5′ and/or 3′ end of gene sequences may be used. The deletions are preferably of 1-5, 5-10, 10-15, 15-20, 20-25, 25-30, 30-35, 35-40, 40-45, 45-50, 50-60, 60-70, 70-80, 80-90, 90-100, 100-125, 125-150, 150-175, or 175-200 nucleotides from the 5′ and/or 3′ end, although the extent of the deletions would naturally be limited by the length of the sequences and the need to be able to use the polynucleotides for the detection of expression levels.

Other polynucleotides of the invention from the 3′ end of gene sequences include those of primers and optional probes for quantitative PCR. Preferably, the primers and probes are those which amplify a region less than about 750, less than about 700, less than about 650, less than about 6000, less than about 550, less than about 500, less than about 450, less than about 400, less than about 350, less than about 300, less than about 250, less than about 200, less than about 150, less than about 100, or less than about 50 nucleotides from the from the polyadenylation signal or polyadenylation site of a gene or expressed sequence. The size of a PCR amplicon of the invention may be of any size, including at least or about 50, at least or about 100, at least about or 150, at least or about 200, at least or about 250, at least or about 300, at least or about 350, or at least or about 400 consecutive nucleotides, all with inclusion of the portion complementary to the PCR primers used.

Other polynucleotides for use in the practice of the invention include those that have sufficient homology to gene sequences to detect their expression by use of hybridization techniques. Such polynucleotides preferably have about or 95%, about or 96%, about or 97%, about or 98%, or about or 99% identity with the gene sequences to be used. Identity is determined using the BLAST algorithm, as described above. The other polynucleotides for use in the practice of the invention may also be described on the basis of the ability to hybridize to polynucleotides of the invention under stringent conditions of about 30% v/v to about 50% formamide and from about 0.01M to about 0.15M salt for hybridization and from about 0.01M to about 0.15M salt for wash conditions at about 55 to about 65° C. or higher, or conditions equivalent thereto.

In a further embodiment of the invention, a population of single stranded nucleic acid molecules comprising one or both strands of a human gene sequence is provided as a probe such that at least a portion of said population may be hybridized to one or both strands of a nucleic acid molecule quantitatively amplified from RNA of a cell or sample of the invention. The population may be only the antisense strand of a human gene sequence such that a sense strand of a molecule from, or amplified from, a cell may be hybridized to a portion of said population. The population preferably comprises a sufficiently excess amount of said one or both strands of a human gene sequence in comparison to the amount of expressed (or amplified) nucleic acid molecules containing a complementary gene sequence.

The invention further provides a method of classifying a human tumor sample by detecting the expression levels of 50 or more transcribed sequences in a nucleic acid or cell containing sample obtained from a human subject, and classifying the sample as containing a tumor cell of a tumor type found in humans to the exclusion of one or more other human tumor types. In some embodiments, the method may be used to classify a sample as being, or having cells of, one of the 53 tumor types listed above to the exclusion of one or more of the other 52. In other embodiments, the method is used to classify a sample as being, or having cells of, one of the 34 tumor types listed above to the exclusion of one or more of the other 33 tumor types. In further embodiments, the method is used to classify a sample as being, or having cells of, one of the 39 tumor types listed above to the exclusion of one or more of the other 38 tumor types.

The invention also provides a method for classifying tumor samples as being one of a subset of the possible tumor types described herein by detecting the expression levels of 50 or more transcribed sequences in a nucleic acid containing tumor sample obtained from a human subject, and classifying the sample as being one of a number of tumor types found in humans to the exclusion of one or more other human tumor types. In some embodiments of the invention, the number of other tumor types is from 1 to about 3, more preferably from 1 to about 5, from 1 to about 7, or from 1 to about 9 or about 10. In other embodiments, the number of tumor types are all of the same tissue or organ origin such as those listed above. This aspect of the invention is related to the above discussion of FIG. 8 and of trading off specificity in favor of increased confidence, and may be advantageously applied to situations where the classification of a sample as a single tumor type is at a level of accuracy or performance that can be improved by classifying the sample as one of a subset of possible tumor types.

In additional embodiments, the invention may be practiced by analyzing gene expression from single cells or homogenous cell populations which have been dissected away from, or otherwise isolated or purified from, contaminating cells of a sample as present in a simple biopsy. One advantage provided by these embodiments is that contaminating, non-tumor cells (such as infiltrating lymphocytes or other immune system cells) may be removed as so be absent from affecting the genes identified or the subsequent analysis of gene expression levels as provided herein. Such contamination is present where a biopsy is used to generate gene expression profiles.

In further embodiments of the invention utilizing Q-PCR or reverse transcriptase Q-PCR as the assay platform, the expression levels of gene sequences of the invention may be compared to expression levels of reference genes in the same sample or a ratio of expression levels may be used. This provides a means to “normalize” the expression data for comparison of data on a plurality of known tumor types and a cell containing sample to be assayed. While a variety of reference genes may be used, the invention may also be practiced with the use of 8 particular reference gene sequences that were identified for use with the set of 39 tumor types. Moreover, the Q-PCR may be performed in whole or in part with use of a multiplex format.

mRNA sequences corresponding to the 8 reference sequences are provided in the attached Sequence Listing. A listing of the corresponding SEQ ID NOs, with corresponding identifying information, including accession numbers and other information, is provided by the following.

(SEQ ID NO: 253) >Hs.77031_mRNA_1 gi|16741772|gb|BC016680.1|BC016680 Homo sapiens clone MGC: 21349 IMAGE: 4338754 polyA = 3 (SEQ ID NO: 254) >Hs.77541_mRNA_1 gi|12804364|gb|BC003043.1|BC003043 Homo sapiens clone MGC: 4370 IMAGE: 2822973 polyA = 3 (SEQ ID NO: 255) >Hs.7001_mRNA_1 gi|6808256|emb|AL137727.1|HSM802274 Homo sapiens mRNA; cDNA DKFZp434M0519 (from clone DKFZp434M0519); partial cds polyA = 3 (SEQ ID NO: 256) >Hs.302144_mRNA_1 gi|11493400|gb|AF130047.1|AF130047 Homo sapiens clone FLB3020 polyA = 0 (SEQ ID NO: 257) >Hs.26510_mRNA_2 gi|11345385|gb|AF308803.1|AF308803 Homo sapiens chromosome 15 map 15q26 polyA = 3 (SEQ ID NO: 258) >Hs.324709_mRNA_2 gi|12655026|gb|BC001361.1|BC001361 Homo sapiens clone MGC: 2474 IMAGE: 3050694 polyA = 2 (SEQ ID NO: 259) >Hs.65756_mRNA_3 gi|3641494|gb|AF035154.1|AF035154 Homo sapiens chromosome 16 map 16p13.3 polyA = 3 (SEQ ID NO: 260) >Hs.165743_mRNA_2 gi|13543889|gb|BC006091.1|BC006091 Homo sapiens clone MGC: 12673 IMAGE: 3677524 polyA = 3

Detection of expression of any of the above reference sequences may be by the same or different methodology as for the other gene sequences described above. In some embodiments of the invention, the expression levels of gene sequences is measured by detection of expressed sequences in a cell containing sample as hybridizing to the following oligonucleotides, which correspond to the above sequences as indicated by the accession numbers provided.

>BC006091 (SEQ ID NO: 261) TCATCTTCACCAAACCAGTCCGAGGGGTCGAAGCCAGACACGAGAGGAAGAGGGTCCTGG >BC003043 (SEQ ID NO: 262) CTCTGCTCCTGCTCCTGCCTGCATGTTCTCTCTGTTGTTGGAGCCTGGAGCCTTGCTCTC >AF130047 (SEQ ID NO: 263) TGCTCCCGGCTGTCCTCCTCTCCTCTTCCCTAGTGAGTGGTTAATGAGTGTTAATGCCTA >AF035154 (SEQ ID NO: 264) CCCCATCTCTAAAACCAGTAAATCAGCCAGCGAATACCCGGAAGCAAGATGCACAGGCGG >BC001361 (SEQ ID NO: 265) CCAGAAACAAGGAAGAGGAAAGACAAAGGGAAGGGACGGGAGCCCTGGAGAAGCCCGACC >AF308803 (SEQ ID NO: 266) AAGTACAACCCATGCTGCTAAGATGCGAGCAGGAAGAGGCATCCTTTGCTAAATCCTGTT >BC016680 (SEQ ID NO: 267) ACCTCACCCCTGCCCGGCCCAAGCTCTACTTGTGTACAGTGTATATTGTATAATAGACAA >AL137727 (SEQ ID NO: 268) TTCCCTTAATTCCTCCTCCCGACCTTTTTTACCCCCCCAGTTGCAGTATTTAACTGGGCT

In an additional aspect, the methods provided by the present invention may also be automated in whole or in part. This includes the embodiment of the invention in software. Non-limiting examples include processor executable instructions on one or more computer readable storage devices wherein said instructions direct the classification of tumor samples based upon gene expression levels as described herein. Additional processor executable instructions on one or more computer readable storage devices are contemplated wherein said instructions cause representation and/or manipulation, via a computer output device, of the process or results of a classification method.

The invention includes software and hardware embodiments wherein the gene expression data of a set of gene sequences in a plurality of known tumor types is embodied as a data set. In some embodiments, the gene expression data set is used for the practice of a method of the invention. The invention also provides computer related means and systems for performing the methods disclosed herein. In some embodiments, an apparatus for classifying a cell containing sample is provided. Such an apparatus may comprise a query input configured to receive a query storage configured to store a gene expression data set, as described herein, received from a query input; and a module for accessing and using data from the storage in a classification algorithm as described herein. The apparatus may further comprise a string storage for the results of the classification algorithm, optionally with a module for accessing and using data from the string storage in an output algorithm as described herein.

The steps of a method, process, or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The various steps or acts in a method or process may be performed in the order shown, or may be performed in another order. Additionally, one or more process or method steps may be omitted or one or more process or method steps may be added to the methods and processes. An additional step, block, or action may be added in the beginning, end, or intervening existing elements of the methods and processes.

A further aspect of the invention provides for the use of the present invention in relation to clinical activities. In some embodiments, the determination or measurement of gene expression as described herein is performed as part of providing medical care to a patient, including the providing of diagnostic services in support of providing medical care. Thus the invention includes a method in the medical care of a patient, the method comprising determining or measuring expression levels of gene sequences in a cell containing sample obtained from a patient as described herein. The method may further comprise the classifying of the sample, based on the determination/measurement, as including a tumor cell of a tumor type or tissue origin in a manner as described herein. The determination and/or classification may be for use in relation to any aspect or embodiment of the invention as described herein.

The determination or measurement of expression levels may be preceded by a variety of related actions. In some embodiments, the measurement is preceded by a determination or diagnosis of a human subject as in need of said measurement. The measurement may be preceded by a determination of a need for the measurement, such as that by a medical doctor, nurse or other health care provider or professional, or those working under their instruction, or personnel of a health insurance or maintenance organization in approving the performance of the measurement as a basis to request reimbursement or payment for the performance.

The measurement may also be preceded by preparatory acts necessary to the actual measuring. Non-limiting examples include the actual obtaining of a cell containing sample from a human subject; or receipt of a cell containing sample; or sectioning a cell containing sample; or isolating cells from a cell containing sample; or obtaining RNA from cells of a cell containing sample; or reverse transcribing RNA from cells of a cell containing sample. The sample may be any as described herein for the practice of the invention.

In additional embodiments, the invention provides for a method of ordering, or receiving an order for, the performance of a method in the medical care of a patient or other method of the invention. The ordering may be made by a medical doctor, a nurse, or other health care provider, or those working under their instruction, while the receiving, directly or indirectly, may be made by any person who performs the method(s). The ordering may be by any means of communication, including communication that is written, oral, electronic, digital, analog, telephonic, in person, by facsimile, by mail, or otherwise passes through a jurisdiction within the United States.

The invention further provides methods in the processing of reimbursement or payment for a test, such as the above method in the medical care of a patient or other method of the invention. A method in the processing of reimbursement or payment may comprise indicating that 1) payment has been received, or 2) payment will be made by another payer, or 3) payment remains unpaid on paper or in a database after performance of an expression level detection, determination or measurement method of the invention. The database may be in any form, with electronic forms such as a computer implemented database included within the scope of the invention. The indicating may be in the form of a code on paper or in the database. The “another payer” may be any person or entity beyond that to whom a previous request for reimbursement or payment was made.

Alternative, the method may comprise receiving reimbursement or payment for the technical or actual performance of the above method in the medical care of a patient; for the interpretation of the results from said method; or for any other method of the invention. Of course the invention also includes embodiments comprising instructing another person or party to receive the reimbursement or payment. The ordering may be by any communication means, including those described above. The receipt may be from any entity, including an insurance company, health maintenance organization, governmental health agency, or a patient as non-limiting examples. The payment may be in whole or in part. In the case of a patient, the payment may be in the form of a partial payment known as a co-pay.

In yet another embodiment, the method may comprise forwarding or having forwarded a reimbursement or payment request to an insurance company, health maintenance organization, governmental health agency, or to a patient for the performance of the above method in the medical care of a patient or other method of the invention. The request may be by any communication means, including those described above.

In a further embodiment, the method may comprise receiving indication of approval for payment, or denial of payment, for performance of the above method in the medical care of a patient or other method of the invention. Such an indication may come from any person or party to whom a request for reimbursement or payment was made. Non-limiting examples include an insurance company, health maintenance organization, or a governmental health agency, like Medicare or Medicaid as non-limiting examples. The indication may be by any communication means, including those described above.

An additional embodiment is where the method comprises sending a request for reimbursement for performance of the above method in the medical care of a patient or other method of the invention. Such a request may be made by any communication means, including those described above. The request may have been made to an insurance company, health maintenance organization, federal health agency, or the patient for whom the method was performed.

A further method comprises indicating the need for reimbursement or payment on a form or into a database for performance of the above method in the medical care of a patient or other method of the invention. Alternatively, the method may simply indicate the performance of the method. The database may be in any form, with electronic forms such as a computer implemented database included within the scope of the invention. The indicating may be in the form of a code on paper or in the database.

In the above methods in the medical care of a patient or other method of the invention, the method may comprise reporting the results of the method, optionally to a health care facility, a health care provider or professional, a doctor, a nurse, or personnel working therefor. The reporting may also be directly or indirectly to the patient. The reporting may be by any means of communication, including those described above.

The invention further provides kits for the determination or measurement of gene expression levels in a cell containing sample as described herein. A kit will typically comprise one or more reagents to detect gene expression as described herein for the practice of the present invention. Non-limiting examples include polynucleotide probes or primers for the detection of expression levels, one or more enzymes used in the methods of the invention, and one or more tubes for use in the practice of the invention. In some embodiments, the kit will include an array, or solid media capable of being assembled into an array, for the detection of gene expression as described herein. In other embodiments, the kit may comprise one or more antibodies that is immunoreactive with epitopes present on a polypeptide which indicates expression of a gene sequence. In some embodiments, the antibody will be an antibody fragment.

A kit of the invention may also include instructional materials disclosing or describing the use of the kit or a primer or probe of the present invention in a method of the invention as provided herein. A kit may also include additional components to facilitate the particular application for which the kit is designed. Thus, for example, a kit may additionally contain means of detecting the label (e.g. enzyme substrates for enzymatic labels, filter sets to detect fluorescent labels, appropriate secondary labels such as a sheep anti-mouse-HRP, or the like). A kit may additionally include buffers and other reagents recognized for use in a method of the invention.

Having now generally described the invention, the same will be more readily understood through reference to the following examples which are provided by way of illustration, and are not intended to be limiting of the present invention, unless specified.

EXAMPLES Example 1 Materials and Methods

The following table shows the types and number of samples of known tumors used in Example 2.

Tumor type Number of samples Adrenal 7 Brain-glial 16 Brain-Meningioma 7 Breast 43 Cervix-adeno 8 Cervix-squamous 13 Endometrium 13 GallBladder 5 Germ-cell 22 GIST 10 Kidney 11 Leiomyosarcoma 13 Liver 14 Lung-adeno 9 Lung-large 9 Lung-small 8 Lung-squamous 10 Lymphoma-B 7 Lymphoma-Hodgkins 9 Lymphoma-T 5 Mesothelioma 10 Osteosarcoma 7 Ovary-clear 14 Ovary-serous 14 Pancreas 24 Prostate 11 Skin-basal-cell 5 Skin-melanoma 10 Skin-squamous 6 Small-and-large-bowel 42 Soft-tissue-Liposarcoma 5 Soft-tissue-MFH 11 Soft-tissue-Sarcoma-synovial 7 Stomach-adeno 9 Testis-Seminoma 10 Thyroid-follicular-papillary 12 Thyroid-medullary 7 UrinaryBladder 25 Total 468 Bile-Duct 1 Cholangiocarcinoma 4 Esophagus 2 Esophagus-Barretts 4 Esophagus-squamous 4 HN-squamous 3 Ovary (unclassified) 1 Ovary-endometriod 1 Ovary-mucinous 4 Ovary-stromal 1 Soft-tissue-Ewings-sarcoma 2 Soft-tissue-Fibrosarcoma 2 Soft-tissue- 3 Rhabdomyosarcoma Total 32

The 500 samples were fresh or frozen samples of tumor containing tissue. The 468 samples shown above were used for further experiments by taking 374 as the training set and the remaining 94 samples as the testing set. Tumor types of fewer than 5 samples were not used initially.

The samples contained both primary and metastatic tumors with a confirmed diagnosis. A single 5 μm section was stained (H+E), and the tumor visualized. Pure tumor populations were obtained by either manual dissection, or laser capture microdissection (Arcturus, Mountain View, Calif.).

RNA extraction and quality control were performed on each sample. Briefly, samples were processed using a silica spin column-based extraction method (Arcturus, Mountain View, Calif.). The total quantity of RNA extracted was assessed using quantitative PCR (Taqman, ABI), with primers specific for β-actin transcription. Only samples with greater than 10 ng of RNA were amplified.

Samples were amplified using a modified RNA polymerase 2-round amplification protocol (Arcturus, Mountain View, Calif.). Following amplification, the RNA product yield was quantitated by OD(260/280) spectroscopy, and the amplified product visualized by agarose (2%) denaturing gel electrophoresis.

The amplified product from each sample was then hybridized to a microarray to detect the level of transcript expression in the samples. Random gene selection was performed using random sampling function software. For each number of genes selected, random samples were selected 100 times and used to compute the cross-validation and predictive accuracies on both training and testing sets. Cross-validation was by dividing the training set into parts with one being used to train and another being used as a test.

Example 2 Results

The mean of the accuracies from 100 samplings and the 95% confidence interval were calculated and plotted for each step from 50 to 16948 genes. The plots showed the cross-validation and predictive accuracies from KNN (k-nearest neighbor) algorithm versus the number of genes selected by chance. Random gene selection used random sampling function in R software.

50 or more genes were capable of accurately classifying among the numerous tumor types in toto with a better than 50% accuracy. Similar results are observed with the use of the samples and KNN with known FFPE tumor specimens from which RNA was extracted and analyzed for gene expression.

It should be noted that while the accuracy stabilized with the use of additional genes, it is expected that there are particular sets of 50 or more genes that have significantly higher accuracies. Classification of additional tumor types, such as those totaling 32 samples in the table above, may be made with the inclusion of additional samples.

The accuracy level of a set of 100 randomly selected expressed gene sequences was determined to be 66% and was used as described in Example 3 to generate FIGS. 1 and 2.

Example 3 Information Capacity of Random Gene Sets

Subsets of the 100 randomly selected expressed gene sequences used to classify among 39 tumor types were tested for their ability to classify among subsets of the 39 tumor types. The expression levels of random combinations of 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 and all 100 (each combination sampled 10 times) of the 100 expressed sequences were used with data from tumor types and then used to predict test random sets of tumor samples (each sampled 10 times) ranging from 2 to all 39 types. FIG. 1 shows the classification capability of various gene sets are shown relative to the number of tumor types classified. As expected, a higher number of gene sequences are needed to classify tumor types with higher accuracies. FIG. 2 shows the classification performance for various numbers of tumor types relative to the number of gene sequences used.

The GenBank accession numbers of the 100 gene sequences are AF269223, BC006286, AK025501, AJ002367, AI469140, AW013883, NM_(—)001238, AI476350, BC006546, AI041212, BF724944, AI376951, R56211, BC006393, X13274, BC001133, N62397, BC000885, AK001588, AK057901, AF146760, AI951287, AK025604, BC007581, BC015025, R43102, AW449550, AI922539, AI684144, AI277662, BC015999, AW444656, BC011612, BC015401, BF447279, BC009956, AL050163, BC001248, BE672684, AL137353, BC001340, U45975, BE856598, BC009060, AL137728, AA713797, AL583913, AK054617, AI028262, AI753041, BG939593, AL080179, AA814915, AF131798, AI961568, BC009849, AK021603, BC012561, AI570494, BC006973, AW294857, BC004952, AK026535, AI923614, AW082090, AI005513, AF339768, AK023167, AF169693, AF076249, BC007662, BC015520, AI814187, AI565381, AW271626, AK024120, AF139065, BC014075, AI887245, AF257081, AI767898, AF070634, AF155132, X69804, U65579, NM_(—)004933, AI655104, AW131780, AI650407, AF131774, AA814057, AJ311123, BC009702, AF264036, AL161961, AJ010857, AF106912, AK023542, AF073518, and D83032. They were indexed from 1 to 100, and representative, and non-limiting, random sets used in the invention are as follows:

For 50-genes, set 1, genes 9, 52, 55, 24, 44, 58, 20, 79, 81, 86, 22, 84, 27, 32, 73, 70, 18, 41, 54, 38, 46, 78, 87, 49, 15, 95, 12, 23, 30, 13, 36, 98, 28, 56, 21, 19, 35, 51, 25, 43, 99, 34, 64, 66, 82, 72, 11, 92, 59, and 71 were used. In set 2, genes 72, 92, 27, 8, 14, 87, 42, 83, 65, 85, 40, 21, 74, 66, 6, 28, 13, 98, 91, 78, 49, 52, 33, 30, 97, 84, 2, 95, 88, 64, 93, 11, 1, 45, 61, 39, 12, 67, 53, 89, 43, 17, 54, 7, 55, 38, 3, 15, 70, and 31 were used. In set 3, genes 9, 35, 87, 52, 73, 74, 88, 22, 41, 28, 93, 15, 67, 20, 68, 17, 46, 43, 51, 24, 84, 79, 19, 100, 76, 6, 49, 97, 16, 59, 89, 66, 45, 63, 2, 27, 13, 98, 69, 60, 26, 86, 83, 58, 71, 54, 82, 32, 42, and 77 were used. In set 4, genes 34, 67, 48, 53, 24, 61, 6, 64, 89, 76, 35, 21, 86, 83, 68, 7, 25, 65, 58, 28, 97, 90, 31, 57, 3, 50, 2, 96, 84, 29, 42, 46, 82, 62, 19, 95, 44, 52, 33, 36, 15, 37, 70, 11, 43, 13, 8, 49, 16, and 99 were used. In set 5, genes 11, 22, 87, 25, 5, 38, 35, 68, 94, 51, 60, 53, 20, 42, 95, 92, 33, 15, 14, 24, 85, 37, 69, 17, 19, 93, 8, 97, 46, 83, 26, 86, 66, 89, 63, 16, 74, 28, 52, 2, 96, 99, 71, 10, 65, 90, 29, 34, 77, and 45 were used. In set 6, genes 62, 6, 69, 12, 19, 50, 51, 5, 1, 32, 41, 84, 27, 10, 93, 28, 79, 21, 88, 47, 58, 64, 74, 39, 33, 46, 17, 86, 87, 4, 60, 98, 97, 45, 26, 72, 40, 63, 30, 54, 52, 11, 15, 96, 14, 24, 73, 67, 59, and 38 were used. In set 7, genes 67, 21, 62, 15, 59, 6, 23, 30, 89, 94, 82, 74, 96, 17, 41, 38, 48, 100, 5, 71, 20, 55, 79, 28, 44, 64, 92, 65, 51, 37, 32, 22, 72, 98, 12, 54, 78, 50, 60, 76, 88, 3, 40, 80, 77, 16, 24, 42, 8, and 14 were used. In set 8, genes 43, 68, 8, 38, 82, 73, 12, 23, 77, 63, 56, 33, 66, 14, 47, 17, 53, 62, 42, 57, 30, 89, 44, 58, 34, 24, 81, 40, 45, 1, 99, 52, 37, 80, 96, 10, 71, 50, 20, 51, 18, 54, 31, 70, 84, 3, 83, 76, 59, and 91 were used. In set 9, genes 36, 90, 34, 79, 29, 24, 44, 51, 27, 58, 52, 37, 68, 49, 89, 80, 57, 8, 22, 77, 54, 65, 26, 91, 21, 64, 59, 61, 13, 74, 87, 50, 63, 20, 78, 23, 96, 67, 30, 55, 81, 35, 72, 56, 95, 82, 39, 42, 88, and 92 were used. In set 10, genes 59, 94, 91, 88, 3, 45, 13, 96, 66, 58, 60, 69, 21, 95, 4, 7, 67, 83, 44, 2, 37, 24, 8, 12, 53, 47, 34, 9, 31, 46, 11, 68, 1, 6, 29, 14, 33, 54, 43, 80, 39, 18, 100, 10, 84, 65, 5, 76, 26, and 22 were used.

For 55 genes, set 1, genes 20, 76, 33, 73, 15, 83, 47, 2, 95, 67, 26, 49, 97, 25, 46, 13, 51, 42, 14, 11, 39, 94, 37, 100, 56, 63, 6, 66, 45, 75, 3, 78, 55, 7, 72, 44, 35, 48, 65, 38, 60, 90, 30, 36, 77, 23, 16, 32, 80, 89, 8, 91, 43, 50, and 28 were used. In set 2, genes 11, 63, 93, 79, 21, 57, 66, 10, 42, 83, 75, 94, 3, 38, 49, 91, 53, 90, 50, 52, 39, 99, 85, 48, 31, 18, 89, 25, 87, 56, 40, 5, 19, 88, 27, 92, 20, 100, 59, 43, 95, 80, 86, 44, 55, 68, 54, 33, 96, 45, 2, 9, 81, 73, and 37 were used. In set 3, genes 20, 73, 76, 29, 44, 33, 84, 98, 15, 69, 32, 14, 50, 70, 63, 41, 87, 74, 99, 34, 23, 36, 37, 68, 89, 43, 91, 18, 26, 45, 9, 90, 28, 92, 7, 30, 22, 54, 96, 72, 16, 38, 58, 52, 56, 79, 57, 47, 83, 17, 49, 2, 80, 51, and 46 were used. In set 4, genes 90, 63, 60, 82, 81, 50, 25, 24, 56, 9, 8, 89, 70, 55, 15, 4, 35, 75, 77, 46, 87, 6, 49, 85, 98, 58, 28, 27, 64, 47, 99, 51, 86, 21, 54, 80, 41, 74, 88, 14, 36, 2, 23, 32, 19, 30, 52, 84, 62, 37, 43, 53, 72, 39, and 92 were used. In set 5, genes 27, 43, 33, 84, 89, 31, 60, 97, 15, 45, 42, 73, 4, 6, 90, 61, 72, 56, 2, 38, 96, 74, 94, 14, 25, 77, 58, 86, 21, 32, 82, 3, 50, 17, 28, 48, 44, 7, 70, 20, 59, 83, 1, 71, 52, 95, 69, 54, 39, 46, 63, 51, 57, 34, and 22 were used. In set 6, genes 96, 12, 94, 27, 11, 33, 25, 22, 26, 50, 60, 70, 68, 30, 82, 34, 17, 32, 29, 19, 87, 76, 81, 7, 55, 35, 45, 56, 31, 99, 5, 24, 54, 97, 21, 92, 98, 36, 88, 23, 58, 77, 14, 95, 9, 73, 84, 61, 2, 38, 83, 65, 42, 74, and 48 were used. In set 7, genes 52, 11, 79, 27, 23, 64, 96, 33, 75, 12, 34, 94, 26, 78, 67, 51, 57, 70, 28, 89, 9, 98, 62, 91, 41, 65, 73, 74, 8, 16, 90, 37, 1, 10, 59, 81, 63, 30, 80, 18, 15, 48, 36, 19, 84, 14, 45, 38, 97, 99, 3, 82, 54, 22, and 5 were used. In set 8, genes 83, 57, 6, 37, 44, 76, 5, 59, 74, 62, 72, 23, 93, 75, 32, 100, 98, 29, 30, 65, 21, 17, 78, 46, 13, 82, 14, 50, 66, 63, 90, 49, 54, 68, 60, 10, 87, 94, 58, 91, 33, 31, 36, 8, 11, 92, 51, 38, 43, 52, 7, 86, 89, 84, and 70 were used. In set 9, genes 29, 100, 79, 21, 63, 12, 51, 2, 18, 77, 81, 33, 68, 69, 13, 23, 37, 39, 14, 3, 93, 36, 5, 35, 30, 40, 28, 61, 49, 71, 27, 99, 75, 96, 83, 97, 78, 54, 19, 89, 62, 38, 8, 53, 26, 43, 52, 25, 58, 9, 31, 86, 65, 6, and 60 were used. In set 10, genes 7, 37, 22, 39, 41, 89, 57, 75, 6, 23, 47, 51, 55, 93, 49, 5, 15, 79, 20, 11, 42, 87, 78, 33, 68, 76, 94, 77, 62, 16, 31, 54, 28, 99, 90, 61, 25, 21, 59, 73, 83, 95, 30, 91, 65, 24, 4, 17, 10, 72, 63, 98, 34, 69, and 1 were used.

For 60 genes, set 1, genes 67, 60, 53, 20, 3, 9, 87, 16, 1, 14, 96, 82, 79, 94, 35, 32, 44, 22, 17, 46, 59, 29, 40, 57, 68, 52, 48, 31, 34, 23, 91, 38, 92, 49, 51, 86, 88, 55, 50, 39, 83, 65, 11, 42, 4, 63, 47, 73, 84, 75, 77, 18, 74, 100, 26, 5, 72, 10, 90, and 76 were used. In set 2, genes 62, 67, 70, 82, 8, 10, 26, 45, 98, 38, 76, 14, 72, 36, 89, 95, 86, 96, 18, 91, 75, 74, 7, 46, 16, 83, 65, 33, 29, 57, 32, 42, 34, 37, 80, 100, 99, 9, 2, 22, 64, 11, 87, 35, 23, 55, 60, 61, 81, 49, 5, 58, 3, 40, 71, 54, 85, 94, 66, and 20 were used. In set 3, genes 49, 10, 76, 94, 83, 90, 42, 57, 38, 85, 29, 1, 60, 71, 65, 30, 64, 23, 72, 27, 70, 13, 100, 43, 20, 44, 4, 88, 79, 24, 84, 91, 87, 41, 21, 48, 54, 68, 16, 35, 6, 89, 2, 34, 96, 22, 99, 52, 28, 3, 15, 47, 7, 61, 63, 75, 19, 97, 56, and 39 were used. In set 4, genes 99, 94, 58, 51, 46, 87, 77, 23, 9, 74, 52, 4, 47, 42, 5, 62, 48, 14, 35, 32, 75, 98, 95, 18, 67, 76, 50, 8, 1, 19, 22, 72, 11, 83, 82, 89, 12, 24, 90, 80, 92, 85, 26, 66, 38, 78, 79, 60, 49, 59, 25, 84, 36, 29, 45, 55, 27, 70, 39, and 57 were used. In set 5, genes 39, 21, 70, 81, 88, 30, 2, 57, 45, 5, 47, 93, 1, 34, 51, 49, 3, 6, 65, 97, 41, 67, 95, 85, 98, 29, 82, 38, 17, 84, 72, 52, 20, 33, 53, 66, 7, 54, 25, 23, 80, 61, 76, 9, 14, 48, 26, 12, 32, 4, 64, 73, 56, 87, 59, 35, 31, 62, 13, and 15 were used. In set 6, genes 99, 80, 35, 87, 17, 27, 53, 43, 38, 45, 61, 34, 81, 3, 16, 42, 24, 37, 19, 39, 59, 6, 28, 74, 32, 92, 18, 31, 25, 66, 79, 41, 51, 97, 58, 7, 49, 70, 71, 33, 78, 85, 63, 72, 89, 15, 40, 29, 46, 1, 73, 68, 56, 54, 47, 5, 65, 100, 44, and 22 were used. In set 7, genes 15, 51, 66, 47, 4, 82, 78, 71, 72, 75, 61, 10, 34, 18, 12, 55, 32, 80, 45, 14, 3, 62, 20, 74, 96, 48, 94, 88, 69, 64, 86, 9, 24, 41, 8, 28, 81, 13, 37, 87, 53, 44, 57, 43, 30, 38, 67, 5, 100, 91, 50, 2, 42, 77, 7, 83, 73, 99, 68, and 6 were used. In set 8, genes 41, 21, 20, 62, 50, 86, 13, 23, 94, 45, 80, 51, 42, 52, 47, 76, 18, 72, 25, 8, 35, 58, 37, 32, 46, 71, 99, 33, 48, 77, 38, 19, 44, 66, 7, 53, 12, 10, 74, 96, 84, 28, 30, 15, 2, 81, 7, 26, 79, 88, 24, 49, 65, 17, 95, 63, 75, 11, 55, and 36 were used. In set 9, genes 14, 40, 30, 48, 37, 3, 28, 57, 58, 22, 70, 74, 91, 98, 46, 76, 81, 65, 54, 23, 11, 34, 17, 53, 26, 67, 80, 42, 86, 73, 25, 24, 9, 88, 38, 45, 13, 56, 83, 87, 31, 36, 43, 100, 35, 41, 16, 33, 61, 6, 49, 63, 71, 64, 96, 8, 19, 39, 68, and 84 were used. In set 10, genes 97, 39, 83, 8, 35, 74, 13, 96, 20, 19, 69, 10, 81, 57, 65, 17, 12, 48, 86, 4, 94, 25, 92, 22, 55, 43, 34, 45, 73, 18, 31, 15, 2, 61, 51, 91, 89, 82, 68, 46, 24, 77, 27, 88, 72, 16, 37, 70, 29, 60, 80, 14, 23, 44, 49, 66, 62, 32, 28, and 98 were used.

For 65 genes, set 1, genes 68, 57, 82, 75, 62, 43, 41, 76, 59, 34, 78, 95, 32, 79, 88, 46, 4, 89, 96, 84, 66, 10, 31, 23, 52, 16, 85, 98, 28, 25, 74, 69, 39, 63, 64, 58, 65, 30, 13, 19, 40, 50, 48, 6, 93, 2, 11, 51, 100, 26, 27, 24, 1, 87, 91, 38, 5, 21, 56, 35, 61, 17, 90, 94, and 83 were used. In set 2, genes 62, 33, 59, 65, 12, 97, 20, 99, 13, 64, 29, 23, 49, 35, 66, 74, 77, 46, 14, 11, 81, 32, 42, 34, 70, 17, 54, 44, 24, 53, 3, 8, 71, 47, 96, 80, 86, 40, 15, 37, 90, 67, 73, 50, 25, 51, 36, 75, 72, 92, 93, 4, 84, 18, 76, 21, 38, 88, 68, 9, 60, 52, 45, 7, and 41 were used. In set 3, genes 12, 80, 56, 70, 50, 95, 15, 85, 93, 53, 45, 47, 10, 99, 32, 76, 67, 89, 83, 35, 91, 62, 6, 84, 23, 52, 65, 9, 37, 4, 51, 42, 48, 49, 100, 21, 5, 43, 75, 92, 98, 36, 16, 27, 19, 22, 82, 73, 58, 63, 34, 74, 3, 71, 87, 72, 81, 1, 68, 46, 55, 88, 64, 11, and 33 were used. In set 4, genes 16, 41, 15, 40, 19, 47, 77, 96, 5, 21, 38, 84, 22, 27, 81, 46, 74, 36, 8, 52, 98, 87, 91, 54, 86, 80, 25, 39, 75, 42, 10, 83, 51, 90, 62, 78, 17, 9, 53, 68, 12, 100, 24, 89, 20, 58, 59, 11, 92, 32, 30, 95, 49, 55, 73, 82, 99, 70, 97, 13, 6, 93, 67, 29, and 45 were used. In set 5, genes 94, 3, 31, 85, 51, 80, 8, 55, 22, 93, 97, 49, 14, 81, 67, 76, 77, 75, 19, 59, 5, 72, 34, 62, 58, 43, 7, 44, 35, 98, 24, 74, 41, 73, 63, 13, 87, 56, 15, 42, 12, 91, 50, 37, 29, 40, 53, 83, 2, 99, 100, 1, 10, 33, 16, 26, 9, 71, 39, 11, 46, 57, 66, 92, and 82 were used. In set 6, genes 86, 55, 15, 9, 13, 94, 33, 16, 14, 11, 32, 59, 88, 64, 90, 50, 45, 82, 7, 44, 48, 98, 21, 51, 62, 99, 75, 25, 19, 41, 24, 26, 17, 23, 6, 71, 72, 47, 42, 2, 85, 22, 56, 81, 78, 79, 43, 18, 100, 36, 34, 70, 39, 80, 66, 97, 58, 31, 30, 57, 35, 96, 12, 29, and 10 were used. In set 7, genes 16, 50, 4, 18, 60, 65, 37, 94, 1, 88, 76, 71, 31, 2, 53, 59, 19, 26, 28, 89, 87, 77, 63, 57, 92, 55, 20, 93, 72, 38, 46, 62, 45, 11, 52, 95, 54, 14, 36, 42, 39, 64, 7, 99, 86, 78, 27, 43, 66, 58, 25, 81, 79, 41, 90, 13, 73, 67, 32, 44, 23, 34, 29, 6, and 35 were used. In set 8, genes 8, 53, 3, 33, 84, 61, 74, 98, 31, 9, 55, 62, 4, 88, 27, 50, 85, 34, 69, 83, 99, 17, 25, 19, 40, 90, 45, 30, 28, 92, 93, 75, 95, 37, 6, 24, 79, 96, 70, 60, 91, 52, 89, 49, 10, 100, 39, 77, 41, 23, 29, 20, 22, 5, 16, 59, 21, 46, 80, 32, 73, 72, 2, 26, and 48 were used. In set 9, genes 98, 82, 24, 35, 25, 93, 5, 56, 76, 96, 2, 78, 40, 13, 83, 86, 92, 77, 81, 29, 58, 99, 97, 80, 18, 27, 1, 65, 14, 16, 59, 20, 26, 67, 32, 22, 90, 37, 85, 7, 41, 34, 4, 68, 45, 12, 79, 62, 17, 75, 84, 91, 54, 72, 57, 10, 95, 44, 52, 9, 28, 89, 100, 33, and 21 were used. In set 10, genes 96, 40, 22, 50, 75, 38, 98, 89, 55, 60, 86, 18, 87, 85, 49, 2, 57, 73, 33, 29, 59, 42, 63, 68, 62, 92, 74, 53, 8, 7, 51, 71, 11, 30, 83, 56, 77, 81, 79, 16, 37, 69, 61, 64, 27, 67, 25, 100, 31, 3, 13, 4, 12, 21, 65, 99, 36, 66, 6, 94, 44, 35, 72, 95, and 90 were used.

For 70 genes, set 1, genes 36, 6, 100, 39, 37, 3, 27, 45, 93, 19, 89, 43, 68, 9, 60, 46, 51, 80, 32, 52, 62, 35, 58, 14, 10, 33, 85, 12, 64, 67, 75, 86, 17, 44, 83, 24, 87, 84, 23, 96, 79, 20, 13, 8, 11, 76, 88, 56, 38, 98, 29, 16, 99, 2, 66, 30, 48, 26, 5, 25, 78, 42, 47, 94, 15, 4, 55, 65, 97, and 71 were used. In set 2, genes 96, 98, 38, 32, 52, 25, 31, 14, 91, 53, 8, 94, 49, 27, 69, 20, 44, 4, 92, 56, 61, 97, 18, 65, 66, 54, 21, 3, 29, 79, 80, 70, 77, 50, 39, 99, 58, 23, 85, 51, 15, 72, 33, 19, 24, 68, 7, 41, 81, 64, 57, 73, 84, 46, 22, 74, 11, 45, 55, 82, 6, 47, 59, 42, 88, 9, 16, 34, 83, and 30 were used. In set 3, genes 27, 46, 30, 54, 47, 94, 26, 38, 73, 31, 43, 8, 50, 48, 6, 56, 59, 25, 89, 52, 78, 68, 49, 29, 83, 92, 97, 98, 4, 3, 95, 87, 23, 1, 51, 44, 34, 35, 85, 61, 22, 84, 42, 13, 75, 93, 45, 88, 19, 80, 39, 24, 77, 2, 55, 62, 11, 90, 18, 81, 57, 20, 96, 28, 7, 70, 86, 5, 63, and 69 were used. In set 4, genes 65, 29, 88, 19, 42, 30, 15, 16, 74, 53, 25, 8, 95, 5, 69, 99, 59, 67, 84, 14, 80, 12, 37, 13, 71, 39, 43, 100, 60, 79, 51, 11, 45, 82, 83, 61, 62, 90, 6, 20, 2, 18, 97, 1, 48, 81, 35, 87, 56, 36, 93, 41, 54, 46, 10, 27, 47, 33, 55, 64, 26, 57, 85, 89, 9, 96, 72, 68, 23, and 32 were used. In set 5, genes 25, 41, 56, 91, 19, 22, 63, 39, 59, 83, 7, 74, 20, 86, 84, 2, 43, 73, 69, 58, 35, 26, 23, 42, 29, 10, 13, 77, 16, 72, 71, 81, 40, 66, 80, 50, 12, 48, 64, 100, 24, 94, 97, 57, 98, 68, 78, 92, 53, 31, 45, 38, 61, 75, 5, 1, 44, 99, 3, 36, 88, 34, 21, 17, 15, 89, 37, 51, 85, and 79 were used. In set 6, genes 59, 78, 34, 83, 5, 11, 60, 97, 3, 9, 20, 90, 33, 8, 31, 10, 80, 7, 92, 15, 23, 72, 14, 86, 82, 18, 42, 88, 94, 48, 79, 73, 77, 52, 95, 16, 87, 28, 98, 71, 74, 21, 67, 6, 66, 35, 99, 29, 32, 75, 26, 39, 47, 45, 50, 41, 54, 1, 84, 85, 91, 100, 61, 12, 37, 4, 25, 55, 46, and 13 were used. In set 7, genes 63, 14, 66, 75, 12, 2, 90, 81, 27, 72, 70, 89, 59, 46, 6, 53, 22, 80, 30, 79, 82, 71, 92, 19, 73, 83, 38, 40, 1, 68, 20, 8, 50, 74, 94, 26, 35, 28, 43, 34, 77, 18, 96, 16, 95, 85, 15, 9, 11, 84, 39, 10, 54, 65, 57, 25, 60, 51, 55, 33, 17, 44, 29, 58, 93, 62, 21, 4, 7, and 78 were used. In set 8, genes 60, 76, 17, 29, 68, 24, 54, 87, 16, 66, 15, 8, 85, 92, 67, 100, 82, 74, 41, 33, 3, 35, 94, 78, 58, 75, 98, 63, 95, 12, 47, 81, 91, 9, 7, 83, 77, 22, 89, 56, 49, 31, 96, 2, 70, 23, 46, 6, 39, 90, 59, 71, 44, 10, 36, 52, 42, 86, 5, 64, 55, 69, 84, 28, 93, 53, 38, 27, 13, and 26 were used. In set 9, genes 21, 24, 41, 29, 92, 30, 51, 31, 83, 71, 37, 23, 11, 53, 14, 93, 45, 69, 52, 56, 70, 68, 3, 79, 26, 58, 66, 15, 50, 95, 16, 2, 4, 5, 28, 42, 34, 9, 82, 6, 63, 44, 87, 32, 59, 80, 55, 96, 54, 89, 22, 94, 36, 46, 40, 86, 98, 38, 67, 85, 35, 60, 25, 1, 78, 61, 17, 64, 7, and 91 were used. In set 10, genes 93, 44, 77, 3, 31, 64, 39, 89, 23, 51, 78, 85, 35, 81, 22, 74, 97, 14, 27, 13, 16, 88, 28, 61, 57, 79, 99, 37, 30, 36, 24, 11, 45, 34, 54, 50, 41, 1, 7, 48, 56, 63, 58, 49, 17, 26, 15, 69, 2, 53, 43, 62, 55, 100, 95, 52, 83, 29, 19, 38, 59, 76, 20, 87, 66, 25, 72, 70, 4, and 73 were used.

For 75 genes, set 1, genes 73, 40, 56, 32, 59, 42, 70, 12, 100, 6, 28, 11, 43, 55, 5, 64, 80, 99, 23, 57, 18, 82, 60, 61, 31, 81, 14, 3, 91, 76, 86, 19, 26, 83, 38, 29, 8, 36, 69, 85, 96, 27, 47, 10, 35, 39, 94, 24, 62, 34, 54, 65, 25, 90, 51, 67, 41, 46, 33, 1, 37, 49, 9, 71, 13, 21, 44, 2, 98, 52, 84, 20, 74, 93, and 88 were used. In set 2, genes 26, 21, 43, 56, 15, 55, 9, 34, 58, 12, 85, 44, 20, 99, 74, 35, 39, 88, 53, 8, 92, 67, 6, 48, 69, 28, 23, 87, 71, 5, 72, 89, 38, 100, 25, 1, 13, 3, 14, 29, 96, 62, 64, 90, 78, 63, 68, 66, 11, 41, 77, 42, 4, 60, 24, 98, 18, 17, 52, 46, 30, 32, 70, 33, 31, 83, 45, 36, 84, 95, 82, 80, 22, 50, and 73 were used. In set 3, genes 96, 11, 58, 14, 77, 32, 6, 28, 55, 12, 40, 72, 83, 7, 89, 67, 51, 63, 95, 15, 74, 99, 88, 81, 84, 38, 36, 13, 87, 5, 69, 62, 19, 86, 90, 76, 66, 33, 52, 4, 20, 78, 59, 27, 17, 2, 43, 75, 64, 79, 53, 26, 3, 42, 100, 48, 71, 85, 41, 25, 61, 57, 49, 70, 37, 80, 24, 94, 30, 54, 9, 35, 21, 16, and 22 were used. In set 4, genes 48, 31, 73, 90, 10, 100, 32, 56, 83, 38, 93, 7, 53, 8, 79, 15, 63, 5, 92, 76, 58, 59, 35, 67, 2, 98, 23, 37, 24, 94, 25, 9, 46, 36, 82, 40, 89, 27, 34, 71, 84, 97, 86, 6, 21, 54, 22, 72, 17, 44, 26, 57, 64, 11, 91, 75, 80, 95, 62, 88, 51, 39, 99, 69, 43, 68, 42, 52, 16, 4, 30, 77, 81, 60, and 50 were used. In set 5, genes 86, 46, 90, 79, 40, 99, 53, 67, 97, 82, 7, 15, 49, 71, 94, 48, 68, 80, 20, 51, 19, 96, 100, 38, 91, 83, 50, 33, 76, 66, 93, 22, 74, 85, 45, 31, 10, 62, 84, 25, 88, 77, 43, 78, 69, 24, 61, 57, 41, 56, 63, 32, 16, 59, 12, 4, 14, 28, 87, 44, 65, 55, 98, 35, 9, 64, 75, 47, 89, 18, 52, 36, 29, 54, and 81 were used. In set 6, genes 70, 47, 96, 46, 43, 2, 66, 39, 54, 40, 31, 84, 92, 30, 5, 75, 21, 9, 4, 24, 59, 90, 42, 44, 45, 97, 55, 69, 74, 79, 87, 86, 91, 56, 13, 98, 12, 64, 34, 99, 67, 83, 27, 68, 16, 10, 81, 61, 80, 7, 94, 82, 49, 71, 53, 15, 76, 36, 11, 19, 41, 65, 8, 28, 14, 95, 62, 51, 63, 88, 3, 60, 18, 58, and 52 were used. In set 7, genes 90, 80, 39, 46, 51, 91, 25, 16, 3, 36, 20, 30, 17, 99, 95, 44, 27, 89, 61, 9, 65, 19, 86, 13, 84, 14, 5, 10, 82, 67, 85, 45, 59, 81, 35, 41, 4, 71, 32, 24, 22, 6, 53, 98, 54, 66, 42, 18, 97, 94, 87, 49, 79, 56, 72, 57, 76, 69, 28, 43, 23, 11, 52, 92, 7, 93, 96, 75, 73, 8, 58, 83, 50, 29, and 68 were used. In set 8, genes 95, 93, 14, 43, 31, 32, 100, 6, 92, 28, 68, 99, 35, 60, 90, 70, 22, 49, 54, 94, 56, 4, 97, 85, 2, 46, 11, 50, 63, 30, 38, 76, 39, 58, 64, 67, 83, 33, 88, 79, 87, 40, 57, 27, 55, 18, 3, 29, 82, 53, 98, 91, 61, 80, 26, 84, 20, 77, 86, 51, 1, 74, 23, 19, 10, 21, 47, 69, 24, 66, 81, 96, 15, 36, and 41 were used. In set 9, genes 33, 41, 48, 68, 53, 45, 30, 79, 23, 70, 86, 13, 71, 92, 58, 1, 77, 26, 61, 81, 69, 14, 73, 88, 44, 87, 74, 9, 4, 12, 20, 75, 60, 57, 55, 82, 22, 94, 46, 65, 16, 19, 52, 40, 59, 66, 64, 28, 96, 91, 93, 39, 72, 5, 98, 6, 3, 62, 24, 36, 49, 31, 47, 90, 35, 89, 84, 99, 32, 11, 56, 17, 83, 51, and 97 were used. In set 10, genes 40, 10, 67, 9, 43, 13, 52, 73, 50, 41, 54, 56, 98, 100, 83, 85, 28, 32, 47, 66, 74, 65, 79, 81, 94, 36, 90, 69, 31, 64, 88, 99, 44, 18, 33, 75, 95, 42, 58, 92, 15, 53, 97, 34, 63, 30, 24, 3, 45, 29, 82, 48, 17, 14, 26, 49, 93, 27, 87, 6, 57, 39, 68, 12, 70, 4, 25, 91, 11, 89, 21, 23, 96, 84, and 46 were used.

For 80 genes, set 1, genes 75, 2, 91, 94, 19, 31, 43, 50, 96, 49, 29, 14, 93, 58, 69, 82, 28, 6, 65, 26, 66, 40, 64, 34, 33, 53, 13, 4, 37, 80, 57, 59, 1, 87, 11, 16, 83, 21, 35, 52, 25, 99, 45, 46, 36, 89, 88, 7, 39, 55, 90, 72, 17, 9, 85, 44, 22, 56, 8, 23, 18, 77, 12, 10, 48, 97, 61, 74, 92, 81, 95, 68, 47, 71, 62, 24, 70, 20, 79, and 32 were used. In set 2, genes 1, 34, 89, 27, 22, 77, 28, 35, 11, 7, 39, 21, 46, 49, 74, 43, 13, 75, 14, 65, 73, 92, 19, 66, 29, 81, 88, 78, 40, 32, 12, 71, 9, 44, 23, 70, 45, 10, 98, 48, 68, 55, 82, 5, 56, 59, 15, 95, 33, 99, 87, 85, 18, 97, 100, 83, 53, 63, 6, 2, 37, 17, 67, 62, 50, 42, 25, 94, 31, 69, 90, 84, 64, 16, 57, 51, 54, 80, 86, and 38 were used. In set 3, genes 63, 28, 35, 67, 96, 9, 12, 31, 1, 59, 22, 44, 11, 82, 6, 64, 87, 47, 21, 94, 42, 2, 72, 19, 20, 27, 89, 13, 77, 3, 16, 79, 38, 10, 80, 52, 50, 33, 25, 4, 30, 40, 32, 36, 8, 43, 26, 51, 18, 66, 61, 68, 56, 74, 53, 7, 73, 88, 49, 23, 46, 76, 92, 93, 83, 70, 24, 98, 97, 58, 65, 29, 55, 91, 95, 90, 5, 69, 86, and 78 were used. In set 4, genes 79, 72, 68, 31, 42, 95, 78, 36, 10, 34, 59, 91, 46, 40, 82, 1, 44, 4, 69, 3, 17, 43, 35, 63, 18, 13, 77, 81, 67, 26, 60, 86, 25, 61, 89, 76, 55, 27, 22, 29, 20, 11, 7, 30, 54, 39, 62, 8, 74, 28, 71, 12, 38, 65, 66, 64, 21, 9, 56, 16, 88, 99, 96, 32, 94, 51, 90, 37, 87, 92, 97, 70, 41, 57, 50, 45, 83, 24, 48, and 58 were used. In set 5, genes 100, 69, 33, 24, 83, 84, 97, 22, 40, 45, 17, 3, 43, 52, 50, 30, 8, 99, 9, 46, 7, 14, 35, 61, 15, 16, 64, 6, 23, 41, 60, 63, 96, 98, 38, 36, 49, 13, 76, 85, 87, 71, 66, 56, 80, 20, 34, 29, 57, 91, 81, 78, 27, 88, 37, 94, 51, 5, 1, 74, 44, 70, 58, 25, 19, 89, 39, 47, 65, 62, 68, 95, 18, 75, 79, 59, 2, 10, 73, and 53 were used. In set 6, genes 69, 100, 3, 35, 58, 56, 96, 43, 39, 50, 61, 36, 71, 95, 30, 18, 90, 63, 21, 31, 94, 46, 44, 23, 7, 10, 88, 49, 9, 53, 25, 54, 2, 97, 82, 75, 68, 48, 26, 91, 70, 65, 51, 19, 84, 29, 47, 12, 99, 85, 20, 16, 5, 22, 73, 93, 92, 89, 62, 81, 77, 41, 83, 1, 72, 27, 15, 79, 67, 37, 11, 64, 87, 86, 80, 74, 55, 8, 13, and 60 were used. In set 7, genes 67, 73, 85, 95, 92, 60, 29, 28, 24, 90, 72, 71, 37, 76, 27, 78, 53, 34, 98, 70, 87, 33, 5, 41, 42, 68, 62, 82, 100, 96, 69, 65, 6, 91, 21, 38, 3, 80, 25, 75, 31, 52, 79, 20, 84, 83, 19, 86, 57, 9, 77, 58, 64, 97, 14, 8, 50, 2, 51, 94, 56, 46, 35, 93, 7, 39, 1, 88, 59, 17, 48, 74, 32, 81, 99, 16, 11, 49, 13, and 30 were used. In set 8, genes 80, 52, 14, 42, 21, 76, 32, 69, 30, 60, 86, 61, 48, 24, 67, 92, 16, 75, 93, 2, 6, 99, 20, 73, 9, 97, 98, 56, 47, 12, 35, 26, 36, 41, 96, 55, 11, 84, 7, 87, 4, 70, 79, 88, 44, 17, 50, 27, 89, 28, 29, 43, 77, 39, 8, 15, 91, 65, 22, 71, 53, 37, 34, 95, 83, 45, 68, 1, 18, 13, 31, 85, 3, 90, 51, 49, 19, 66, 63, and 54 were used. In set 9, genes 91, 22, 68, 85, 53, 89, 10, 77, 97, 4, 7, 33, 46, 51, 14, 76, 82, 62, 17, 3, 65, 70, 84, 75, 31, 50, 73, 63, 19, 52, 42, 26, 23, 47, 96, 2, 64, 56, 9, 54, 38, 93, 13, 90, 86, 8, 59, 57, 79, 28, 21, 88, 5, 66, 1, 94, 55, 35, 15, 87, 74, 32, 27, 92, 72, 18, 69, 80, 37, 67, 71, 34, 95, 99, 40, 83, 30, 81, 48, and 39 were used. In set 10, genes 92, 76, 86, 5, 20, 1, 48, 42, 62, 29, 12, 7, 37, 46, 47, 82, 32, 66, 97, 77, 56, 91, 30, 80, 36, 72, 17, 31, 2, 81, 23, 28, 51, 55, 98, 40, 95, 13, 10, 58, 33, 21, 14, 74, 85, 88, 22, 75, 94, 27, 43, 3, 100, 61, 67, 4, 25, 6, 44, 60, 24, 93, 63, 89, 70, 41, 15, 11, 53, 87, 16, 65, 52, 68, 57, 99, 50, 45, 71, and 38 were used.

For 85 genes, set 1, genes 38, 35, 85, 59, 17, 7, 31, 58, 96, 97, 16, 70, 82, 42, 21, 54, 88, 34, 63, 4, 27, 29, 3, 19, 69, 36, 9, 99, 74, 86, 76, 24, 15, 81, 73, 93, 40, 52, 26, 57, 37, 87, 55, 90, 41, 79, 45, 77, 91, 71, 61, 11, 94, 83, 25, 48, 1, 5, 8, 22, 33, 46, 60, 56, 20, 44, 89, 18, 10, 23, 78, 65, 50, 72, 75, 47, 98, 28, 66, 68, 32, 12, 51, 13, and 100 were used. In set 2, genes 32, 90, 94, 21, 77, 63, 17, 27, 62, 41, 35, 81, 100, 14, 45, 69, 3, 75, 34, 76, 65, 15, 95, 86, 39, 92, 89, 24, 57, 4, 54, 50, 58, 88, 5, 56, 22, 59, 6, 52, 28, 1, 9, 40, 98, 99, 91, 19, 8, 23, 96, 2, 73, 67, 7, 25, 53, 12, 44, 18, 13, 87, 60, 49, 93, 55, 20, 72, 42, 66, 30, 80, 33, 26, 64, 46, 84, 31, 70, 61, 71, 83, 38, 36, and 29 were used. In set 3, genes 88, 20, 1, 58, 53, 32, 65, 34, 50, 75, 71, 36, 59, 39, 30, 61, 8, 62, 14, 3, 94, 66, 35, 37, 17, 47, 77, 60, 4, 80, 74, 28, 97, 87, 93, 33, 64, 48, 29, 18, 49, 21, 56, 69, 22, 25, 43, 54, 91, 7, 81, 79, 12, 85, 96, 40, 63, 52, 82, 86, 41, 24, 44, 84, 70, 6, 15, 38, 57, 16, 55, 90, 76, 42, 51, 23, 11, 67, 45, 98, 19, 10, 27, 2, and 31 were used. In set 4, genes 64, 86, 54, 83, 47, 21, 67, 57, 73, 23, 71, 76, 56, 9, 44, 75, 82, 11, 8, 99, 72, 13, 79, 28, 92, 5, 27, 90, 24, 91, 33, 68, 51, 60, 94, 58, 78, 48, 18, 42, 53, 98, 70, 32, 41, 49, 45, 6, 30, 63, 95, 80, 36, 87, 97, 65, 77, 3, 26, 35, 59, 40, 84, 37, 61, 81, 39, 46, 22, 1, 2, 50, 25, 69, 4, 43, 15, 29, 20, 17, 88, 10, 38, 100, and 19 were used. In set 5, genes 11, 92, 15, 42, 33, 19, 6, 57, 23, 87, 31, 5, 30, 21, 54, 51, 14, 68, 97, 34, 59, 24, 20, 50, 29, 65, 13, 80, 16, 73, 8, 25, 47, 55, 27, 45, 100, 96, 85, 38, 37, 81, 44, 4, 9, 70, 98, 77, 48, 35, 28, 79, 41, 71, 86, 61, 2, 49, 60, 67, 66, 69, 72, 3, 83, 26, 1, 89, 17, 39, 52, 10, 32, 75, 82, 99, 40, 95, 90, 53, 22, 91, 62, 78, and 56 were used. In set 6, genes 87, 32, 4, 63, 15, 81, 92, 10, 74, 44, 7, 23, 89, 93, 28, 59, 50, 72, 30, 60, 54, 71, 39, 12, 21, 85, 40, 37, 68, 64, 97, 66, 52, 67, 98, 91, 1, 83, 61, 6, 24, 38, 86, 77, 26, 88, 43, 100, 48, 20, 14, 31, 82, 9, 13, 62, 55, 45, 57, 11, 27, 90, 25, 80, 17, 5, 94, 42, 53, 49, 29, 99, 78, 2, 84, 73, 58, 75, 18, 19, 65, 3, 47, 41, and 36 were used. In set 7, genes 56, 38, 23, 74, 34, 99, 93, 4, 13, 18, 61, 49, 20, 5, 76, 88, 91, 31, 78, 32, 1, 89, 12, 16, 51, 54, 81, 70, 86, 97, 66, 19, 59, 39, 8, 80, 73, 35, 71, 77, 24, 53, 68, 33, 62, 69, 43, 41, 15, 94, 44, 52, 29, 100, 55, 36, 27, 25, 67, 21, 96, 30, 42, 92, 11, 3, 45, 63, 72, 57, 47, 46, 75, 90, 2, 48, 14, 6, 9, 87, 22, 98, 95, 84, and 65 were used. In set 8, genes 79, 64, 71, 18, 37, 40, 54, 34, 26, 65, 39, 67, 14, 62, 95, 11, 49, 92, 59, 48, 6, 12, 57, 9, 20, 81, 16, 50, 38, 33, 100, 47, 63, 3, 84, 87, 35, 98, 56, 93, 66, 23, 2, 29, 90, 78, 85, 60, 19, 72, 97, 36, 13, 94, 25, 45, 41, 27, 69, 52, 8, 68, 46, 30, 1, 96, 7, 83, 80, 4, 99, 15, 76, 10, 58, 89, 88, 51, 55, 82, 53, 28, 44, 73, and 77 were used. In set 9, genes 35, 85, 81, 4, 20, 88, 66, 74, 13, 36, 6, 24, 95, 97, 2, 21, 90, 57, 89, 42, 73, 79, 64, 59, 46, 68, 92, 67, 82, 28, 56, 14, 65, 99, 39, 38, 8, 62, 61, 78, 11, 48, 93, 91, 29, 33, 76, 16, 69, 47, 84, 94, 7, 54, 30, 32, 23, 70, 52, 43, 51, 41, 60, 100, 27, 63, 75, 77, 80, 5, 3, 44, 10, 87, 40, 71, 37, 72, 1, 53, 22, 83, 49, 17, and 34 were used. In set 10, genes 23; 39, 86, 48, 65, 73, 24, 27, 61, 37, 99, 64, 58, 74, 3, 22, 57, 60, 13, 93, 44, 100, 66, 69, 38, 83, 6, 81, 59, 36, 68, 95, 71, 70, 84, 62, 96, 26, 30, 32, 20, 54, 80, 19, 97, 16, 4, 77, 12, 5, 35, 29, 18, 52, 53, 87, 98, 90, 10, 75, 72, 55, 50, 88, 28, 34, 41, 94, 11, 76, 7, 45, 31, 46, 49, 9, 82, 17, 79, 1, 25, 40, 67, 47, and 85 were used.

For 90 genes, set 1, genes 79, 5, 27, 100, 96, 11, 32, 63, 42, 68, 13, 65, 88, 75, 17, 64, 82, 72, 37, 45, 98, 2, 90, 94, 1, 87, 73, 86, 69, 92, 3, 25, 29, 84, 60, 50, 39, 4, 95, 47, 12, 10, 33, 22, 77, 71, 57, 97, 38, 89, 91, 53, 51, 9, 67, 44, 7, 78, 34, 85, 15, 41, 54, 49, 62, 76, 83, 46, 59, 23, 24, 8, 14, 26, 30, 52, 18, 6, 66, 31, 20, 93, 36, 16, 61, 28, 74, 43, 56, and 48 were used. In set 2, genes 95, 28, 46, 62, 91, 99, 53, 65, 66, 60, 22, 29, 50, 2, 93, 33, 54, 57, 92, 24, 9, 4, 69, 5, 8, 58, 88, 43, 6, 100, 51, 18, 16, 45, 81, 44, 68, 14, 59, 82, 63, 73, 30, 86, 98, 13, 84, 94, 1, 55, 38, 83, 3, 37, 11, 89, 77, 85, 26, 97, 12, 21, 40, 96, 56, 41, 10, 42, 64, 17, 76, 27, 49, 20, 87, 34, 75, 15, 74, 35, 19, 31, 39, 48, 23, 67, 78, 32, 7, and 80 were used. In set 3, genes 88, 89, 6, 94, 17, 60, 8, 76, 45, 90, 47, 80, 15, 85, 51, 5, 46, 36, 65, 4, 25, 67, 78, 77, 97, 23, 11, 40, 61, 53, 39, 12, 38, 21, 59, 55, 32, 34, 71, 69, 20, 50, 93, 3, 30, 29, 75, 73, 49, 98, 58, 43, 18, 95, 42, 82, 66, 16, 33, 37, 92, 52, 56, 41, 87, 99, 74, 24, 86, 48, 81, 57, 83, 26, 79, 68, 13, 63, 72, 9, 70, 14, 54, 100, 64, 19, 96, 7, 31, and 2 were used. In set 4, genes 19, 33, 41, 40, 70, 51, 14, 48, 42, 12, 90, 4, 32, 60, 89, 64, 45, 86, 73, 16, 50, 5, 9, 72, 81, 3, 27, 87, 76, 58, 29, 31, 13, 21, 55, 18, 6, 62, 56, 96, 47, 63, 37, 98, 28, 91, 36, 82, 39, 100, 68, 25, 88, 11, 93, 35, 66, 24, 43, 59, 8, 65, 74, 30, 10, 22, 17, 99, 49, 44, 26, 54, 2, 80, 94, 57, 71, 38, 67, 79, 75, 77, 23, 85, 61, 52, 83, 7, 78, and 53 were used. In set 5, genes 49, 55, 13, 97, 59, 83, 61, 34, 80, 19, 12, 65, 86, 72, 89, 25, 39, 77, 82, 47, 22, 48, 20, 11, 23, 84, 31, 4, 54, 91, 8, 87, 33, 14, 32, 45, 68, 27, 51, 28, 96, 1, 100, 92, 37, 29, 64, 15, 7, 98, 60, 53, 17, 69, 24, 75, 81, 74, 5, 18, 26, 78, 62, 94, 88, 46, 73, 44, 63, 52, 9, 93, 76, 6, 95, 99, 42, 50, 66, 38, 90, 70, 35, 57, 85, 58, 16, 43, 30, and 10 were used. In set 6, genes 81, 52, 60, 16, 18, 40, 67, 47, 58, 51, 26, 5, 53, 34, 24, 68, 14, 43, 49, 69, 99, 73, 29, 96, 37, 62, 66, 38, 88, 48, 11, 50, 79, 74, 15, 39, 83, 57, 94, 95, 100, 12, 84, 10, 33, 3, 93, 91, 17, 46, 59, 86, 7, 9, 71, 19, 22, 80, 27, 97, 4, 75, 89, 21, 78, 85, 63, 61, 77, 31, 32, 56, 6, 72, 92, 55, 76, 90, 36, 35, 98, 1, 82, 25, 23, 44, 65, 64, 28, and 42 were used. In set 7, genes 51, 1, 54, 94, 93, 56, 22, 29, 53, 67, 88, 82, 16, 44, 65, 21, 14, 35, 48, 91, 12, 97, 31, 74, 6, 99, 86, 26, 28, 19, 72, 58, 24, 34, 5, 38, 81, 11, 49, 39, 3, 89, 75, 64, 96, 52, 59, 69, 42, 78, 33, 100, 2, 25, 66, 77, 90, 40, 71, 9, 4, 57, 13, 36, 10, 50, 17, 87, 15, 47, 60, 46, 63, 68, 70, 23, 80, 37, 30, 92, 7, 32, 27, 43, 98, 84, 8, 61, 73, and 41 were used. In set 8, genes 53, 63, 17, 43, 6, 44, 95, 58, 78, 13, 3, 15, 28, 41, 12, 93, 2, 92, 23, 42, 62, 57, 33, 8, 65, 49, 80, 81, 50, 71, 74, 39, 4, 70, 77, 51, 84, 21, 30, 36, 46, 75, 47, 94, 16, 67, 55, 1, 26, 52, 60, 19, 59, 90, 96, 14, 87, 37, 40, 66, 88, 73, 29, 10, 5, 56, 100, 45, 31, 34, 22, 64, 91, 54, 48, 25, 98, 61, 18, 72, 69, 27, 68, 99, 83, 35, 24, 82, 85, and 38 were used. In set 9, genes 62, 91, 49, 28, 69, 38, 19, 35, 89, 3, 24, 79, 32, 12, 47, 40, 39, 50, 86, 6, 44, 65, 33, 70, 16, 41, 21, 53, 72, 74, 87, 14, 51, 7, 60, 67, 100, 42, 93, 36, 2, 57, 76, 20, 25, 27, 95, 18, 73, 97, 54, 99, 63, 66, 96, 22, 77, 56, 90, 81, 61, 17, 48, 23, 15, 4, 30, 45, 59, 8, 71, 52, 85, 92, 46, 98, 64, 94, 75, 83, 13, 26, 43, 84, 5, 1, 29, 68, 82, and 31 were used. In set 10, genes 45, 10, 63, 9, 18, 7, 70, 50, 22, 52, 91, 88, 5, 38, 17, 80, 54, 92, 20, 19, 24, 8, 13, 40, 15, 21, 87, 72, 12, 14, 2, 53, 46, 93, 4, 44, 99, 76, 47, 32, 60, 27, 23, 81, 78, 68, 36, 71, 64, 30, 95, 82, 90, 26, 74, 86, 100, 89, 62, 37, 66, 35, 83, 94, 31, 43, 65, 84, 11, 67, 25, 33, 61, 79, 97, 16, 75, 73, 98, 57, 28, 59, 1, 96, 51, 41, 69, 3, 56, and 55 were used.

For 95 genes, set 1, genes 35, 64, 32, 25, 20, 69, 88, 42, 97, 6, 23, 86, 98, 93, 16, 44, 53, 51, 91, 21, 70, 73, 31, 81, 74, 14, 29, 66, 4, 87, 11, 94, 52, 95, 56, 63, 18, 8, 78, 100, 62, 99, 39, 89, 17, 50, 71, 10, 90, 65, 84, 83, 60, 48, 22, 5, 92, 13, 15, 24, 27, 37, 57, 33, 38, 82, 3, 9, 30, 1, 34, 7, 40, 68, 67, 58, 28, 47, 46, 19, 12, 43, 41, 61, 76, 96, 72, 36, 75, 54, 45, 80, 49, 79, and 55 were used. In set 2, genes 58, 44, 39, 62, 1, 19, 61, 33, 84, 36, 91, 21, 53, 30, 63, 35, 92, 45, 11, 87, 10, 82, 96, 64, 8, 32, 42, 78, 69, 59, 24, 72, 48, 66, 15, 27, 49, 75, 40, 47, 57, 52, 31, 95, 97, 94, 26, 5, 93, 34, 60, 81, 88, 29, 23, 67, 76, 6, 98, 37, 74, 43, 100, 20, 18, 12, 13, 51, 41, 54, 14, 2, 68, 99, 3, 38, 70, 77, 50, 4, 17, 22, 9, 83, 71, 85, 25, 79, 46, 86, 7, 73, 16, 65, and 28 were used. In set 3, genes 15, 4, 25, 94, 92, 77, 78, 70, 17, 52, 36, 23, 44, 98, 39, 99, 59, 50, 75, 16, 82, 48, 18, 90, 10, 72, 8, 34, 9, 19, 1, 57, 93, 46, 54, 69, 32, 21, 81, 91, 28, 38, 68, 3, 41, 47, 87, 63, 24, 13, 84, 5, 65, 67, 74, 62, 85, 12, 53, 30, 73, 51, 2, 80, 29, 26, 83, 43, 55, 86, 88, 89, 35, 66, 31, 96, 100, 58, 60, 14, 6, 61, 49, 22, 20, 27, 7, 64, 37, 45, 97, 95, 40, 71, and 11 were used. In set 4, genes 21, 78, 42, 23, 84, 10, 64, 36, 48, 26, 79, 71, 72, 39, 49, 56, 44, 20, 47, 82, 63, 1, 91, 2, 8, 40, 96, 18, 68, 9, 57, 28, 100, 89, 60, 75, 70, 73, 25, 15, 46, 85, 86, 97, 32, 94, 65, 90, 74, 98, 16, 45, 3, 6, 31, 77, 41, 11, 12, 35, 95, 93, 53, 50, 30, 61, 81, 92, 80, 54, 13, 38, 58, 14, 52, 22, 76, 83, 5, 17, 37, 69, 66, 87, 19, 88, 51, 34, 59, 99, 24, 33, 27, 4, and 62 were used. In set 5, genes 29, 34, 28, 58, 89, 1, 73, 30, 92, 76, 68, 33, 38, 8, 49, 3, 42, 9, 40, 36, 43, 81, 97, 59, 7, 79, 54, 15, 11, 61, 18, 82, 100, 41, 52, 23, 31, 13, 57, 66, 65, 27, 72, 44, 16, 69, 39, 26, 2, 55, 71, 80, 86, 77, 12, 25, 14, 50, 88, 22, 93, 51, 75, 64, 47, 62, 96, 10, 35, 5, 67, 60, 32, 84, 94, 48, 56, 90, 95, 83, 21, 6, 37, 91, 46, 70, 24, 87, 85, 17, 98, 99, 45, 19, and 63 were used. In set 6, genes 36, 34, 46, 2, 5, 77, 91, 59, 61, 29, 9, 85, 52, 16, 17, 60, 51, 95, 69, 58, 57, 23, 82, 33, 18, 45, 43, 49, 90, 1, 94, 93, 47, 37, 35, 63, 27, 96, 32, 15, 25, 86, 55, 24, 26, 71, 48, 7, 28, 79, 11, 44, 76, 3, 68, 88, 62, 73, 54, 39, 22, 13, 75, 19, 66, 98, 70, 10, 83, 100, 42, 31, 38, 4, 92, 78, 99, 97, 56, 21, 20, 6, 72, 40, 65, 67, 53, 30, 8, 14, 84, 50, 12, 80, and 81 were used. In set 7, genes 26, 7, 14, 64, 91, 50, 8, 48, 23, 29, 34, 28, 9, 20, 74, 97, 27, 63, 25, 66, 60, 43, 92, 61, 58, 46, 68, 49, 21, 98, 2, 41, 52, 1, 51, 77, 53, 69, 36, 93, 62, 55, 17, 38, 31, 40, 76, 54, 71, 5, 99, 83, 82, 78, 42, 15, 24, 70, 84, 100, 73, 10, 59, 33, 96, 4, 56, 3, 94, 75, 90, 13, 32, 65, 89, 79, 19, 30, 11, 87, 37, 95, 12, 6, 88, 80, 18, 47, 81, 72, 44, 16, 86, 85, and 67 were used. In set 8, genes 24, 84, 92, 71, 56, 68, 93, 67, 59, 75, 85, 35, 72, 86, 39, 46, 65, 51, 23, 100, 8, 37, 70, 69, 57, 27, 17, 87, 44, 1, 2, 50, 9, 91, 63, 29, 95, 3, 5, 40, 96, 47, 54, 64, 66, 18, 28, 13, 14, 36, 80, 21, 12, 61, 48, 26, 88, 83, 7, 43, 42, 97, 99, 41, 10, 16, 94, 53, 45, 98, 15, 73, 89, 55, 74, 81, 20, 90, 79, 34, 38, 82, 76, 4, 60, 33, 31, 78, 58, 62, 22, 6, 52, 49, and 19 were used. In set 9, genes 99, 77, 10, 92, 24, 43, 41, 15, 46, 78, 38, 19, 2, 5, 3, 81, 82, 22, 56, 63, 47, 90, 33, 34, 75, 100, 62, 65, 13, 30, 95, 98, 94, 25, 67, 11, 6, 66, 14, 48, 93, 4, 21, 89, 35, 68, 97, 45, 27, 59, 76, 85, 42, 49, 23, 40, 37, 74, 26, 52, 8, 91, 53, 57, 58, 86, 31, 20, 9, 16, 84, 69, 96, 44, 32, 54, 60, 7, 51, 83, 72, 28, 29, 61, 80, 55, 64, 17, 18, 70, 50, 1, 12, 73, and 39 were used. In set 10, genes 76, 1, 12, 25, 77, 24, 100, 17, 66, 65, 26, 29, 60, 91, 63, 52, 6, 30, 8, 72, 82, 68, 15, 16, 54, 43, 59, 34, 89, 20, 44, 87, 70, 56, 3, 28, 74, 86, 7, 2, 33, 35, 46, 67, 58, 22, 49, 21, 75, 14, 27, 64, 90, 42, 73, 36, 97, 40, 11, 37, 51, 19, 83, 45, 47, 50, 55, 23, 80, 61, 95, 71, 78, 32, 81, 93, 98, 62, 92, 99, 9, 4, 53, 84, 18, 13, 41, 57, 88, 5, 79, 38, 39, 31, and 94 were used.

Classification of subsets of the 39 tumor types was performed with use of random selections of tumor types from the group of 39. The expression levels of gene sequence sets as described herein were used to classify random combinations of tumor types. Different random sets of tumor types were used with each of the sets of 100, 74, and 90 gene sequences as described in these examples. Representative, and non-limiting, examples of random sets of from 2 to 20 tumor types used are as follows, where the set of 39 tumor types were indexed from 1 to 39.

For 2 tumor types, set 1 used types 26 and 16. Set 2 used types 8 and 5. Set 3 used types 39 and 8. Set 4 used types 27 and 23. Set 5 used types 8 and 19. Set 6 used 12 and 21. Set 7 used types 30 and 15. Set 8 used types 30 and 5. Set 9 used types 18 and 22. Set 10 used types 27 and 26.

For 4 tumor types, set 1 used types 20, 35, 15 and 7. Set 2 used types 36, 1, 28 and 19. Set 3 used types 13, 4, 12 and 21. Set 4 used types 12, 33, 14 and 28. Set 5 used types 6, 28, 5 and 37. Set 6 used types 5, 25, 36 and 15. Set 7 used types 12, 26, 21 and 19. Set 8 used types 19, 3, and 17. Set 9 used types 18, 10, 8 and 9. Set 10 used types 28, 20, 2 and 22.

For 6 tumor types, set 1 used types 27, 3, 10, 39, 11 and 20. Set 2 used types 33, 10, 20, 32, 13 and 19. Set 3 used types 31, 27, 18, 39, 8 and 16. Set 4 used types 25, 28, 10, 12, 7 and 39. Set 5 used types 14, 13, 28, 24, 30 and 36. Set 6 used types 9, 24, 8, 17, 36 and 26. Set 7 used types 20, 1, 34, 26, 6 and 19. Set 8 used types 12, 13, 3, 17, 34 and 22. Set 9 used types 7, 1, 17, 13, 20 and 34. Set 10 used types 5, 11, 25, 29, 28 and 35.

For 8 tumor types, set 1 used types 34, 33, 28, 3, 23, 25, 9 and 29. Set 2 used types 27, 8, 38, 28, 20, 14, 12 and 9. Set 3 used types 29, 21, 19, 1, 13, 26, 11 and 31. Set 4 used types 25, 17, 7, 20, 34, 8, 28 and 10. Set 5 used types 36, 28, 35, 26, 2, 8, 29 and 7. Set 6 used types 10, 23, 2, 27, 33, 21, 25 and 35. Set 7 used types 10, 18, 38, 2, 6, 7, 19 and 32. Set 8 used types 11, 37, 6, 28, 3, 9, 2 and 16. Set 9 used types 22, 2, 10, 8, 17, 19 and 33. Set 10 used types 35, 39, 8, 10, 37, 4, 36 and 6.

For 10 tumor types, set 1 used types 25, 10, 26, 2, 32, 31, 39, 23, 22 and 18. Set 2 used types 12, 35, 6, 16, 20, 3, 39, 36, 11 and 2. Set 3 used types 34, 1, 15, 29, 5, 39, 2, 12, 25 and 18. Set 4 used types 10, 8, 14, 18, 31, 19, 23, 20, 32 and 33. Set 5 used types 10, 18, 37, 15, 4, 35, 33, 24, 39 and 20. Set 6 used types 22, 16, 4, 3, 18, 21, 1, 25, 37 and 13. Set 7 used types 14, 6, 28, 18, 11, 13, 2, 32, 33 and 19. Set 8 used types 39, 2, 38, 4, 34, 8, 25, 6, 32 and 35. Set 9 used types 3, 10, 11, 16, 6, 15, 18, 14, 12 and 26. Set 10 used types 24, 25, 21, 9, 36, 29, 20, 39, 10 and 37.

For 12 tumor types, set 1 used types 26, 20, 4, 12, 2, 31, 38, 18, 16, 39, 3 and 33. Set 2 used types 25, 16, 4, 9, 29, 27, 14, 24, 21, 7, 23 and 2. Set 3 used types 31, 18, 23, 13, 25, 1, 29, 21, 35, 10, 32 and 39. Set 4 used types 8, 34, 23, 9, 35, 14, 25, 21, 2, 33, 18 and 28. Set 5 used types 6, 11, 21, 8, 5, 7, 19, 32, 3, 13, 36 and 9. Set 6 used types 12, 33, 14, 26, 27, 15, 2, 21, 36, 35, 9 and 39. Set 7 used types 26, 29, 32, 17, 31, 19, 6, 5, 20, 34, 2 and 24. Set 8 used types 17, 12, 8, 22, 28, 9, 27, 29, 14, 35, 4 and 32. Set 9 used types 29, 9, 36, 23, 33, 18, 21, 35, 3, 6, 2 and 1. Set 10 used types 1, 3, 35, 29, 22, 27, 8, 23, 2, 36, 14 and 19.

For 14 tumor types, set 1 used types 9, 26, 38, 25, 31, 3, 15, 14, 17, 33, 12, 35, 39 and 16. Set 2 used types 1, 26, 16, 25, 20, 12, 14, 37, 38, 24, 23, 33, 27 and 35. Set 3 used types 11, 21, 35, 38, 32, 34, 27, 39, 16, 15, 4, 5, 13 and 18. Set 4 used types 27, 5, 13, 28, 18, 17, 15, 20, 29, 37, 21, 36, 25 and 14. Set 5 used types 5, 12, 17, 9, 25, 21, 33, 37, 8, 15, 24, 3, 34 and 28. Set 6 used types 11, 19, 34, 26, 9, 6, 32, 14, 27, 29, 30, 16, 24 and 17. Set 7 used types 31, 26, 11, 18, 19, 20, 9, 8, 5, 36, 12, 6, 27 and 38. Set 8 used types 20, 17, 11, 5, 15, 9, 2, 39, 34, 24, 27, 26, 35 and 10. Set 9 used types 1, 14, 39, 30, 17, 6, 10, 35, 31, 33, 15, 29, 32 and 7. Set 10 used types 1, 19, 24, 28, 34, 12, 13, 18, 32, 11, 14, 21, 22 and 25.

For 16 tumor types, set 1 used types 27, 15, 8, 12, 6, 20, 26, 19, 25, 2, 37, 38, 7, 39, 4 and 33. Set 2 used types 17, 18, 28, 5, 6, 31, 25, 13, 8, 20, 37, 36, 35, 9, 23 and 27. Set 3 used types 23, 37, 34, 14, 16, 27, 32, 33, 21, 38, 4, 30, 24, 22, 17 and 25. Set 4 used types 7, 37, 38, 21, 34, 31, 32, 25, 10, 36, 19, 11, 6, 26, 18 and 35. Set 5 used types 9, 32, 12, 24, 20, 13, 38, 21, 39, 23, 36, 18, 37, 22, 5 and 3. Set 6 used types 14, 21, 5, 17, 6, 20, 18, 35, 22, 10, 3, 23, 13, 2, 34 and 26. Set 7 used types 1, 8, 19, 6, 9, 39, 28, 18, 13, 31, 14, 16, 37, 12, 3 and 25. Set 8 used types 32, 36, 28, 38, 9, 33, 2, 5, 4, 11, 19, 18, 13, 8, 12 and 3. Set 9 used types 9, 14, 10, 5, 28, 32, 23, 6, 39, 3, 17, 8, 19, 1, 31 and 12. Set 10 used types 4, 34, 11, 6, 38, 19, 7, 20, 23, 3, 25, 37, 26, 1, 15 and 12.

For 18 tumor types, set 1 used types 15, 24, 39, 35, 7, 30, 16, 13, 20, 3, 26, 4, 12, 10, 34, 25, 21 and 28. Set 2 used types 21, 23, 29, 11, 10, 19, 13, 28, 4, 20, 17, 24, 30, 12, 39, 34, 31 and 9. Set 3 used types 7, 17, 27, 6, 30, 8, 22, 2, 32, 26, 21, 14, 4, 38, 1, 35, 16 and 28. Set 4 used types 17, 13, 20, 33, 10, 3, 16, 22, 1, 38, 2, 9, 28, 5, 6, 19, 12 and 11. Set 5 used types 4, 35, 21, 25, 18, 17, 8, 14, 31, 30, 9, 1, 2, 23, 36, 29, 32 and 37. Set 6 used types 17, 34, 2, 18, 19, 15, 16, 13, 4, 24, 5, 35, 6, 22, 28, 37, 38 and 1. Set 7 used types 34, 26, 12, 25, 27, 3, 17, 7, 2, 32, 9, 36, 21, 19, 22, 8, 20 and 29. Set 8 used types 12, 34, 38, 25, 17, 22, 14, 39, 10, 7, 31, 2, 3, 11, 29, 30, 16 and 24. Set 9 used types 13, 26, 27, 14, 5, 10, 8, 7, 16, 30, 37, 4, 6, 35, 28, 1, 36 and 20. Set 10 used types 15, 2, 17, 23, 26, 28, 36, 38, 12, 6, 19, 37, 20, 14, 9, 39, 11 and 21.

For 20 tumor types, set 1 used types 25, 13, 21, 15, 37, 20, 12, 28, 9, 10, 26, 22, 14, 24, 16, 7, 39, 34, 33 and 4. Set 2 used types 20, 17, 10, 27, 19, 28, 5, 1, 23, 21, 38, 7, 13, 22, 32, 31, 9, 4, 3 and 24. Set 3 used types 17, 13, 7, 20, 11, 38, 34, 3, 15, 12, 5, 39, 9, 10, 4, 35, 27, 6, 21 and 33. Set 4 used types 6, 13, 17, 26, 1, 7, 33, 5, 10, 32, 3, 23, 35, 4, 14, 28, 12, 38, 8 and 27. Set 5 used types 10, 23, 9, 38, 5, 29, 12, 27, 25, 6, 7, 26, 37, 31, 24, 36, 19, 15, 16 and 11. Set 6 used types 30, 24, 21, 11, 23, 25, 8, 9, 7, 31, 27, 5, 14, 29, 1, 19, 16, 12, 22 and 17. Set 7 used types 26, 13, 23, 19, 22, 11, 25, 21, 33, 20, 6, 17, 2, 10, 31, 34, 27, 37, 7 and 9. Set 8 used types 30, 1, 38, 7, 31, 37, 11, 25, 6, 19, 28, 33, 17, 29, 10, 27, 16, 3, 14 and 15. Set 9 used types 15, 19, 26, 24, 5, 33, 11, 2, 13, 18, 31, 22, 32, 20, 23, 6, 10, 25, 36 and 3. Set 10 used types 24, 25, 21, 29, 14, 18, 31, 2, 20, 39, 23, 9, 38, 12, 6, 32, 22, 26, 33 and 7.

Example 4 Specified Gene Sets

A first set of 74 genes and a second set of 90 genes, where the two sets have 38 members in common, were used in the practice of the invention. The performance of the two sets versus varying numbers of tumor types is shown in FIG. 3.

Random subsets of 50 to all members of the set of 74 expressed gene sequences were evaluated in a manner analogous to that described in Example 3. Again, the expression levels of random combinations of 50, 55, 60, 65, 70, and all 74 (each combination sampled 10 times) of the 74 expressed sequences were used with data from tumor types and then used to predict test random sets of tumor samples (each sampled 10 times) ranging from 2 to all 39 types. The resulting data are shown in FIGS. 4 and 5.

The members of the 74 gene sequences were indexed from 1 to 74, and representative random sets used in the invention are as follows:

For 50-genes, set 1, genes 69, 64, 74, 29, 4, 57, 30, 72, 36, 59, 42, 47, 11, 33, 60, 35, 39, 10, 50, 49, 41, 12, 34, 51, 32, 66, 71, 37, 13, 14, 8, 25, 53, 21, 68, 7, 67, 55, 27, 22, 1, 44, 46, 28, 48, 19, 73, 23, 16, and 3 were used. In set 2, genes 60, 61, 23, 17, 10, 31, 16, 8, 72, 73, 18, 49, 71, 46, 29, 21, 66, 39, 22, 27, 43, 30, 51, 3, 38, 19, 37, 35, 70, 54, 40, 2, 55, 28, 45, 33, 25, 14, 48, 20, 36, 47, 62, 9, 69, 68, 53, 58, 15, and 7 were used. In set 3, genes 53, 68, 31, 2, 62, 17, 49, 71, 6, 56, 3, 66, 23, 21, 33, 30, 45, 73, 74, 11, 58, 27, 64, 18, 72, 42, 7, 28, 34, 43, 38, 65, 12, 47, 16, 40, 41, 36, 54, 61, 19, 63, 25, 46, 59, 9, 39, 55, 22, and 48 were used. In set 4, genes 23, 70, 48, 1, 11, 25, 60, 26, 5, 58, 46, 39, 28, 71, 35, 34, 2, 59, 69, 55, 49, 40, 15, 14, 68, 57, 10, 31, 67, 74, 62, 44, 16, 12, 64, 63, 61, 13, 52, 45, 19, 50, 36, 33, 9, 24, 32, 29, 56, and 72 were used. In set 5, genes 30, 26, 10, 34, 67, 73, 15, 59, 3, 64, 14, 70, 23, 47, 72, 71, 44, 49, 31, 48, 5, 61, 53, 20, 33, 58, 37, 50, 43, 18, 21, 38, 29, 16, 12, 63, 39, 4, 45, 60, 69, 25, 24, 65, 55, 13, 36, 11, 17, and 22 were used. In set 6, genes 43, 34, 61, 19, 35, 56, 24, 3, 23, 15, 13, 69, 1, 67, 42, 41, 64, 25, 63, 28, 8, 53, 38, 71, 6, 36, 68, 14, 18, 65, 51, 33, 4, 60, 5, 22, 40, 30, 50, 37, 29, 17, 27, 11, 9, 66, 62, 57, 59, and 10 were used. In set 7, genes 51, 55, 46, 31, 21, 72, 8, 67, 56, 1, 64, 6, 63, 32, 20, 16, 25, 61, 2, 45, 35, 22, 66, 38, 36, 3, 34, 27, 74, 47, 54, 30, 14, 13, 37, 23, 19, 12, 59, 18, 52, 5, 17, 33, 7, 39, 43, 58, 41, and 10 were used. In set 8, genes 28, 68, 71, 46, 48, 47, 5, 23, 22, 35, 60, 3, 40, 33, 41, 72, 12, 24, 15, 37, 1, 20, 45, 53, 61, 65, 74, 4, 10, 51, 26, 30, 38, 44, 55, 73, 66, 6, 39, 52, 36, 2, 59, 67, 27, 43, 50, 18, 8, and 69 were used. In set 9, genes 73, 51, 67, 63, 24, 55, 42, 61, 13, 29, 23, 64, 49, 53, 19, 2, 43, 11, 15, 31, 58, 40, 38, 46, 44, 4, 27, 41, 28, 69, 8, 26, 5, 68, 37, 70, 25, 62, 22, 52, 1, 57, 54, 34, 16, 71, 9, 65, 14, and 30 were used. In set 10, genes 9, 13, 46, 2, 62, 47, 50, 36, 58, 23, 55, 31, 6, 40, 32, 27, 35, 33, 39, 1, 22, 19, 65, 16, 52, 72, 30, 3, 12, 7, 74, 21, 54, 20, 41, 10, 28, 37, 24, 53, 69, 11, 14, 67, 25, 71, 15, 42, 18, and 73 were used.

For 55 genes, set 1, genes 19, 3, 26, 44, 16, 59, 11, 39, 46, 54, 22, 7, 60, 30, 72, 6, 74, 53, 57, 14, 43, 47, 27, 45, 37, 24, 33, 64, 21, 36, 20, 50, 68, 62, 63, 17, 61, 10, 70, 18, 25, 71, 29, 65, 51, 56, 58, 69, 5, 55, 12, 1, 40, 49, and 13 were used. In set 2, genes 35, 15, 11, 33, 5, 29, 73, 69, 31, 70, 10, 45, 41, 72, 74, 26, 32, 12, 30, 34, 16, 64, 13, 50, 46, 38, 18, 48, 37, 68, 40, 61, 62, 6, 63, 47, 36, 65, 17, 67, 71, 39, 4, 59, 22, 24, 8, 9, 58, 3, 52, 20, 14, 25, and 7 were used. In set 3, genes 7, 19, 50, 62, 47, 74, 22, 26, 37, 8, 41, 53, 52, 67, 16, 40, 54, 34, 30, 46, 25, 55, 31, 3, 69, 38, 29, 65, 45, 43, 51, 68, 18, 57, 21, 5, 32, 20, 27, 73, 66, 10, 49, 24, 12, 13, 11, 71, 60, 23, 63, 35, 48, 39, and 70 were used. In set 4, genes 58, 70, 43, 68, 39, 57, 71, 27, 21, 53, 16, 23, 25, 60, 40, 36, 2, 63, 33, 49, 5, 54, 32, 66, 50, 59, 14, 52, 15, 48, 45, 44, 19, 72, 26, 10, 6, 41, 34, 61, 42, 67, 17, 24, 8, 11, 29, 74, 3, 51, 47, 65, 69, 28, and 1 were used. In set 5, genes 60, 53, 21, 63, 7, 19, 69, 3, 9, 22, 10, 50, 59, 71, 20, 11, 70, 6, 4, 17, 58, 16, 40, 68, 73, 38, 18, 15, 57, 26, 34, 67, 41, 27, 49, 28, 46, 54, 1, 13, 31, 48, 32, 61, 42, 66, 29, 5, 55, 72, 25, 30, 39, 44, and 56 were used. In set 6, genes 4, 36, 17, 47, 16, 6, 14, 51, 65, 42, 31, 38, 26, 15, 70, 28, 41, 72, 30, 3, 29, 55, 34, 32, 54, 24, 48, 39, 22, 57, 37, 23, 71, 61, 50, 21, 27, 53, 25, 40, 20, 69, 58, 66, 46, 1, 43, 12, 33, 63, 18, 68, 10, 56, and 45 were used. In set 7, genes 71, 7, 38, 61, 22, 33, 51, 25, 68, 6, 1, 49, 9, 58, 18, 55, 5, 50, 65, 52, 26, 59, 35, 11, 15, 70, 54, 27, 60, 28, 19, 63, 21, 10, 32, 42, 73, 36, 45, 66, 47, 2, 56, 23, 64, 44, 34, 29, 48, 69, 37, 16, 74, 53, and 43 were used. In set 8, genes 25, 42, 70, 28, 6, 48, 43, 20, 60, 18, 56, 74, 27, 9, 55, 67, 58, 68, 39, 38, 29, 1, 21, 45, 44, 66, 53, 34, 47, 64, 41, 57, 10, 3, 31, 65, 54, 46, 50, 59, 23, 73, 24, 51, 36, 26, 16, 49, 37, 62, 7, 32, 19, 22, and 14 were used. In set 9, genes 49, 65, 20, 59, 21, 45, 54, 29, 51, 50, 17, 37, 55, 47, 57, 9, 8, 18, 11, 10, 25, 1, 30, 68, 5, 6, 74, 70, 60, 53, 48, 39, 4, 23, 27, 73, 35, 40, 41, 44, 24, 3, 58, 19, 14, 13, 33, 63, 62, 46, 2, 12, 72, 36, and 7 were used. In set 10, genes 73, 53, 26, 24, 58, 25, 59, 71, 34, 65, 46, 2, 57, 48, 68, 21, 44, 22, 16, 70, 60, 8, 66, 45, 14, 27, 43, 37, 20, 36, 72, 18, 56, 4, 7, 6, 23, 15, 74, 1, 9, 50, 5, 35, 40, 32, 12, 38, 69, 33, 61, 62, 10, 47, and 39 were used.

For 60 genes, set 1, genes 49, 60, 66, 26, 22, 53, 33, 56, 10, 44, 17, 36, 41, 6, 21, 57, 39, 65, 24, 30, 31, 15, 43, 68, 64, 59, 28, 73, 13, 18, 51, 34, 63, 40, 71, 58, 48, 11, 37, 42, 70, 45, 72, 3, 67, 35, 52, 46, 32, 55, 27, 38, 19, 25, 5, 69, 62, 14, 23, and 4 were used. In set 2, genes 57, 5, 31, 15, 20, 54, 21, 42, 71, 50, 17, 68, 61, 53, 9, 35, 67, 12, 14, 52, 41, 38, 22, 45, 32, 39, 70, 18, 6, 26, 59, 40, 25, 28, 56, 10, 3, 47, 34, 8, 60, 2, 9, 62, 66, 19, 11, 37, 27, 36, 69, 7, 65, 4, 33, 24, 51, 55, 48, and 44 were used. In set 3, genes 37, 54, 44, 66, 36, 1, 61, 62, 47, 69, 4, 30, 31, 11, 8, 63, 38, 16, 65, 25, 74, 21, 34, 60, 20, 71, 12, 19, 43, 15, 27, 57, 6, 55, 64, 22, 14, 39, 53, 23, 17, 28, 51, 56, 40, 29, 58, 48, 42, 59, 68, 5, 35, 50, 72, 10, 45, 32, 33, and 73 were used. In set 4, genes 24, 2, 49, 57, 35, 45, 40, 51, 42, 7, 47, 5, 8, 17, 61, 74, 64, 72, 50, 60, 70, 26, 9, 56, 32, 4, 16, 44, 27, 43, 53, 33, 46, 55, 41, 68, 48, 11, 10, 39, 19, 6, 3, 14, 65, 69, 30, 34, 29, 36, 58, 28, 1, 23, 73, 15, 25, 13, 54, and 18 were used. In set 5, genes 18, 28, 1, 22, 71, 37, 62, 46, 31, 25, 70, 64, 66, 35, 5, 60, 10, 26, 9, 43, 67, 20, 59, 51, 33, 42, 3, 24, 49, 13, 27, 38, 61, 14, 52, 63, 11, 74, 7, 16, 23, 72, 39, 73, 15, 6, 17, 30, 57, 8, 50, 48, 34, 53, 2, 69, 29, 56, 44, and 47 were used. In set 6, genes 33, 74, 12, 7, 49, 25, 38, 1, 8, 4, 48, 26, 58, 54, 21, 50, 72, 45, 62, 66, 36, 13, 42, 5, 39, 17, 28, 23, 67, 41, 29, 73, 19, 56, 51, 69, 10, 16, 55, 14, 24, 64, 22, 59, 52, 35, 2, 31, 3, 9, 27, 71, 30, 32, 53, 11, 40, 61, 15, and 70 were used. In set 7, genes 30, 65, 26, 48, 47, 20, 17, 56, 35, 32, 10, 11, 1, 59, 50, 53, 45, 13, 63, 49, 41, 74, 16, 57, 15, 64, 12, 54, 5, 8, 67, 69, 31, 14, 61, 60, 37, 66, 43, 71, 23, 36, 51, 44, 34, 2, 42, 19, 58, 25, 27, 68, 18, 52, 21, 7, 70, 22, 28, and 62 were used. In set 8, genes 12, 58, 11, 5, 72, 70, 63, 66, 49, 44, 14, 48, 26, 73, 51, 47, 13, 65, 1, 39, 61, 17, 40, 8, 24, 54, 42, 34, 64, 21, 53, 59, 46, 4, 20, 29, 57, 74, 31, 67, 6, 69, 7, 68, 41, 3, 18, 62, 19, 32, 10, 43, 36, 71, 28, 60, 30, 15, 23, and 52 were used. In set 9, genes 7, 20, 69, 12, 58, 40, 70, 57, 3, 37, 6, 16, 61, 11, 13, 31, 55, 17, 49, 22, 36, 47, 44, 18, 45, 68, 25, 72, 19, 14, 39, 46, 30, 59, 56, 5, 66, 2, 41, 51, 9, 54, 35, 15, 26, 27, 23, 65, 4, 63, 1, 60, 21, 74, 24, 43, 38, 64, 50, and 67 were used. In set 10, genes 5, 43, 54, 22, 49, 48, 25, 24, 52, 35, 14, 70, 26, 72, 59, 71, 9, 41, 74, 36, 17, 47, 29, 34, 20, 27, 65, 68, 3, 73, 45, 62, 57, 56, 53, 44, 6, 7, 31, 55, 30, 23, 15, 33, 38, 42, 10, 60, 66, 8, 1, 64, 19, 16, 12, 61, 63, 51, 18, and 2 were used.

For 65 genes, set 1, genes 11, 10, 1, 69, 43, 33, 54, 24, 39, 27, 42, 18, 9, 46, 12, 20, 61, 44, 51, 64, 35, 8, 36, 38, 21, 7, 57, 59, 23, 49, 17, 15, 22, 55, 29, 16, 37, 72, 30, 31, 45, 63, 40, 28, 41, 32, 66, 65, 5, 47, 53, 60, 25, 50, 74, 52, 14, 68, 48, 13, 2, 4, 3, 6, and 67 were used. In set 2, genes 37, 8, 31, 4, 23, 57, 69, 40, 3, 9, 5, 32, 42, 44, 56, 21, 10, 34, 74, 61, 39, 38, 13, 70, 41, 19, 48, 47, 29, 52, 26, 72, 49, 45, 7, 63, 16, 25, 24, 14, 18, 60, 59, 11, 35, 2, 30, 68, 58, 67, 27, 33, 66, 12, 71, 51, 55, 6, 20, 54, 1, 46, 64, 62, and 53 were used. In set 3, genes 24, 19, 35, 57, 27, 8, 23, 30, 65, 32, 59, 29, 4, 47, 17, 53, 34, 54, 73, 14, 20, 63, 43, 3, 38, 61, 31, 49, 25, 42, 41, 51, 18, 7, 40, 39, 33, 50, 70, 28, 13, 74, 36, 45, 64, 5, 16, 58, 1, 66, 62, 46, 15, 12, 72, 21, 2, 68, 71, 9, 44, 26, 37, 6, and 55 were used. In set 4, genes 62, 29, 5, 41, 18, 4, 21, 63, 65, 8, 55, 61, 66, 34, 23, 28, 14, 49, 68, 15, 1, 11, 19, 73, 13, 57, 20, 27, 50, 2, 72, 22, 6, 7, 40, 67, 51, 45, 10, 36, 53, 64, 54, 24, 25, 37, 74, 12, 52, 26, 38, 32, 3, 30, 33, 39, 48, 58, 17, 42, 71, 43, 69, 56, and 9 were used. In set 5, genes 49, 58, 74, 65, 67, 44, 57, 28, 56, 18, 59, 31, 10, 17, 41, 39, 63, 7, 21, 55, 38, 2, 51, 42, 5, 53, 20, 34, 16, 43, 19, 15, 50, 4, 6, 11, 52, 37, 8, 64, 69, 12, 48, 60, 1, 66, 27, 36, 45, 30, 14, 72, 68, 73, 35, 47, 71, 22, 70, 33, 32, 46, 25, 13, and 54 were used. In set 6, genes 7, 44, 23, 68, 46, 30, 10, 4, 3, 53, 22, 38, 50, 26, 55, 49, 20, 11, 73, 12, 62, 63, 43, 69, 6, 61, 52, 25, 65, 16, 47, 34, 33, 28, 42, 58, 29, 39, 31, 1, 36, 13, 5, 60, 35, 19, 40, 18, 59, 64, 41, 70, 72, 57, 67, 9, 74, 8, 14, 71, 45, 56, 32, 51, and 2 were used. In set 7, genes 57, 61, 9, 48, 31, 4, 40, 35, 1, 16, 44, 67, 68, 34, 6, 64, 7, 54, 53, 10, 18, 39, 23, 14, 33, 74, 51, 38, 24, 19, 72, 63, 36, 65, 32, 2, 27, 45, 3, 43, 21, 49, 30, 60, 50, 70, 41, 20, 11, 37, 13, 15, 5, 12, 46, 26, 22, 71, 8, 62, 29, 28, 25, 17, and 52 were used. In set 8, genes 11, 21, 3, 6, 74, 58, 52, 40, 17, 23, 41, 63, 22, 56, 55, 8, 60, 54, 51, 57, 66, 68, 29, 24, 69, 39, 16, 49, 72, 59, 48, 61, 2, 7, 44, 37, 43, 45, 35, 25, 1, 4, 20, 14, 36, 42, 65, 62, 71, 32, 19, 70, 28, 27, 9, 46, 33, 18, 67, 15, 30, 26, 12, 47, and 53 were used. In set 9, genes 48, 27, 64, 55, 30, 2, 33, 16, 31, 21, 57, 50, 63, 17, 44, 29, 4, 6, 60, 65, 23, 19, 58, 68, 25, 59, 14, 7, 42, 12, 69, 45, 53, 73, 56, 34, 41, 3, 18, 5, 72, 70, 40, 37, 62, 43, 51, 24, 52, 20, 39, 8, 13, 26, 10, 66, 54, 22, 49, 61, 11, 46, 32, 67, and 36 were used. In set 10, genes 31, 39, 50, 60, 17, 33, 73, 30, 3, 27, 10, 62, 29, 12, 59, 1, 34, 69, 51, 72, 65, 52, 16, 36, 28, 23, 42, 40, 66, 58, 48, 46, 38, 74, 20, 55, 21, 49, 63, 2, 70, 7, 26, 53, 41, 45, 25, 44, 71, 32, 24, 13, 14, 6, 57, 11, 68, 35, 54, 22, 64, 8, 9, 56, and 37 were used.

For 70 genes, set 1, genes 72, 74, 31, 73, 52, 16, 32, 24, 14, 66, 59, 28, 54, 1, 11, 12, 34, 57, 5, 67, 25, 42, 62, 71, 68, 69, 48, 7, 18, 20, 47, 19, 53, 2, 4, 15, 26, 63, 37, 17, 10, 60, 65, 8, 22, 70, 36, 30, 41, 9, 21, 35, 49, 38, 33, 56, 46, 27, 45, 44, 39, 43, 29, 50, 61, 40, 23, 64, 55, and 3 were used. In set 2, genes 45, 32, 60, 2, 42, 56, 8, 46, 30, 27, 17, 62, 26, 24, 65, 49, 16, 70, 3, 47, 50, 4, 40, 28, 1, 36, 22, 59, 48, 9, 57, 5, 72, 23, 13, 44, 67, 14, 12, 34, 21, 41, 71, 39, 66, 25, 69, 19, 15, 6, 68, 29, 52, 43, 64, 58, 54, 11, 37, 38, 55, 7, 20, 61, 53, 63, 10, 74, 51, and 35 were used. In set 3, genes 66, 71, 40, 62, 60, 51, 61, 5, 19, 15, 34, 13, 18, 8, 28, 59, 35, 54, 2, 55, 29, 22, 41, 37, 45, 64, 48, 7, 73, 27, 30, 69, 63, 23, 25, 42, 1, 24, 14, 38, 4, 70, 53, 3, 36, 12, 74, 68, 26, 57, 33, 17, 67, 72, 52, 58, 46, 39, 43, 56, 65, 10, 44, 11, 20, 47, 50, 9, 21, and 49 were used. In set 4, genes 73, 26, 33, 40, 71, 50, 62, 59, 10, 39, 64, 68, 3, 1, 44, 9, 72, 57, 43, 37, 24, 65, 48, 6, 11, 23, 36, 19, 7, 31, 67, 69, 38, 29, 16, 35, 63, 21, 46, 15, 47, 28, 2, 5, 52, 70, 14, 22, 56, 45, 17, 4, 25, 66, 13, 55, 20, 30, 32, 54, 51, 49, 58, 74, 42, 53, 61, 34, 12, and 60 were used. In set 5, genes 7, 1, 24, 70, 26, 35, 68, 71, 74, 33, 5, 20, 49, 27, 65, 10, 72, 21, 66, 12, 4, 43, 9, 55, 23, 56, 47, 31, 42, 59, 61, 45, 67, 13, 63, 58, 17, 54, 28, 3, 64, 53, 39, 36, 30, 40, 37, 16, 41, 11, 52, 14, 62, 8, 46, 25, 44, 69, 29, 48, 51, 22, 73, 57, 18, 15, 19, 38, 6, and 50 were used. In set 6, genes 41, 36, 1, 27, 9, 51, 4, 38, 8, 25, 73, 5, 7, 22, 68, 30, 6, 33, 65, 70, 21, 26, 60, 62, 63, 54, 57, 74, 58, 44, 11, 31, 53, 34, 10, 48, 23, 3, 42, 35, 49, 13, 71, 17, 50, 28, 19, 20, 40, 64, 56, 43, 69, 59, 39, 66, 72, 46, 32, 2, 14, 47, 52, 45, 15, 37, 12, 16, 24, and 67 were used. In set 7, genes 39, 70, 16, 5, 43, 6, 36, 30, 9, 53, 2, 34, 72, 42, 64, 73, 56, 63, 38, 13, 19, 27, 29, 60, 37, 52, 1, 3, 21, 22, 68, 69, 26, 55, 61, 11, 18, 12, 45, 8, 51, 65, 32, 33, 67, 48, 50, 10, 20, 28, 58, 7, 49, 35, 57, 71, 23, 17, 24, 62, 59, 54, 15, 40, 14, 41, 47, 46, 44, and 4 were used. In set 8, genes 3, 5, 50, 35, 53, 57, 14, 49, 55, 8, 25, 22, 71, 60, 13, 19, 12, 32, 26, 44, 15, 39, 17, 31, 61, 23, 66, 68, 4, 6, 7, 41, 24, 40, 58, 67, 46, 70, 45, 64, 51, 69, 18, 62, 47, 52, 11, 30, 73, 28, 33, 2, 36, 1, 72, 42, 20, 27, 10, 16, 63, 38, 59, 74, 43, 9, 56, 34, 21, and 65 were used. In set 9, genes 18, 49, 70, 46, 29, 9, 52, 53, 64, 28, 37, 27, 7, 57, 44, 19, 72, 61, 67, 30, 62, 47, 2, 39, 8, 65, 26, 14, 63, 4, 20, 59, 45, 15, 10, 3, 16, 58, 25, 38, 60, 71, 66, 32, 23, 55, 69, 12, 33, 6, 42, 36, 22, 48, 24, 68, 41, 17, 54, 13, 21, 51, 73, 74, 40, 43, 1, 11, 56, and 35 were used. In set 10, genes 14, 12, 65, 74, 58, 6, 36, 5, 34, 11, 18, 33, 32, 7, 22, 37, 64, 59, 9, 52, 41, 26, 3, 19, 48, 35, 56, 62, 42, 60, 1, 8, 43, 50, 25, 61, 54, 49, 20, 70, 44, 30, 15, 46, 72, 38, 4, 29, 68, 21, 39, 17, 16, 53, 45, 73, 63, 31, 55, 47, 24, 69, 2, 71, 13, 28, 66, 23, 57, and 40 were used.

A similar experiment was performed with random subsets of 50 to all members of the set of 90 expressed gene sequences. Again, the expression levels of random combinations of 50, 55, 60, 65, 70, and all 90 (each combination sampled 10 times) of the 90 expressed sequences were used with data from tumor types and then used to predict test random sets of tumor samples (each sampled 10 times) ranging from 2 to all 39 types. The resulting data are shown in FIGS. 6 and 7.

The members of the 90 gene sequences were indexed from 1 to 90, and representative random sets used in the invention are as follows:

For 50-genes, set 1, genes 89, 30, 62, 23, 31, 20, 53, 25, 15, 38, 11, 22, 68, 44, 58, 7, 14, 61, 67, 32, 18, 71, 9, 54, 46, 3, 57, 50, 59, 79, 48, 90, 82, 64, 39, 21, 60, 37, 47, 10, 52, 77, 33, 45, 35, 83, 16, 69, 74, and 27 were used. In set 2, genes 25, 17, 64, 82, 23, 5, 77, 48, 72, 63, 34, 60, 61, 35, 58, 19, 56, 83, 8, 13, 38, 89, 59, 62, 88, 71, 11, 29, 31, 68, 65, 67, 78, 44, 27, 81, 24, 1, 18, 55, 85, 46, 41, 14, 84, 26, 16, 21, 12, and 69 were used. In set 3, genes 24, 39, 35, 15, 49, 44, 28, 58, 20, 3, 88, 23, 54, 31, 33, 37, 62, 25, 87, 75, 17, 41, 21, 19, 38, 85, 86, 74, 8, 12, 77, 30, 27, 43, 76, 73, 9, 14, 6, 63, 64, 81, 26, 66, 2, 56, 34, 60, 57, and 61 were used. In set 4, genes 40, 71, 55, 63, 2, 13, 38, 58, 26, 18, 76, 74, 17, 67, 69, 4, 9, 20, 21, 10, 35, 70, 49, 37, 12, 77, 61, 60, 15, 7, 36, 89, 33, 59, 78, 39, 82, 16, 64, 28, 6, 66, 52, 5, 44, 73, 34, 75, 31, and 29 were used. In set 5, genes 16, 37, 57, 18, 29, 66, 54, 6, 44, 70, 20, 65, 5, 61, 72, 83, 85, 58, 87, 73, 23, 76, 25, 68, 49, 24, 79, 89, 55, 75, 47, 19, 33, 39, 21, 63, 84, 32, 77, 40, 12, 11, 42, 50, 1, 9, 78, 3, 74, and 7 were used. In set 6, genes 42, 29, 74, 68, 27, 54, 15, 63, 30, 51, 78, 56, 82, 66, 80, 79, 90, 64, 22, 44, 71, 2, 89, 39, 46, 52, 36, 32, 84, 6, 59, 9, 38, 4, 55, 19, 7, 60, 49, 23, 73, 5, 11, 50, 70, 34, 61, 81, 67, and 28 were used. In set 7, genes 31, 27, 24, 75, 7, 46, 40, 60, 51, 37, 87, 28, 67, 62, 50, 66, 61, 63, 49, 1, 39, 74, 81, 4, 52, 22, 79, 45, 12, 41, 15, 90, 26, 33, 78, 48, 83, 10, 53, 73, 6, 19, 71, 59, 68, 56, 64, 13, 32, and 30 were used. In set 8, genes 88, 57, 5, 4, 1, 43, 12, 32, 66, 81, 90, 19, 51, 18, 55, 9, 29, 75, 11, 73, 23, 61, 6, 79, 69, 60, 13, 62, 8, 71, 2, 52, 67, 59, 87, 33, 80, 21, 14, 89, 39, 65, 56, 38, 47, 31, 84, 25, 45, and 41 were used. In set 9, genes 60, 45, 51, 32, 49, 2, 44, 66, 83, 50, 87, 1, 90, 28, 42, 85, 13, 40, 70, 82, 79, 89, 64, 63, 27, 52, 10, 86, 77, 15, 56, 8, 33, 53, 38, 46, 67, 19, 68, 29, 48, 21, 34, 61, 18, 55, 25, 35, 39, and 80 were used. In set 10, genes 80, 39, 23, 76, 87, 33, 30, 88, 85, 89, 24, 47, 44, 43, 48, 55, 14, 73, 22, 19, 67, 1, 42, 51, 60, 12, 9, 6, 75, 17, 40, 25, 28, 74, 38, 66, 5, 50, 8, 37, 15, 29, 21, 11, 35, 31, 13, 36, 52, and 18 were used.

For 55 genes, set 1, genes 86, 47, 80, 15, 74, 20, 79, 35, 14, 49, 41, 2, 48, 30, 81, 65, 5, 24, 51, 10, 31, 68, 7, 21, 28, 37, 38, 4, 18, 23, 44, 77, 42, 19, 61, 27, 75, 67, 36, 22, 26, 50, 32, 58, 71, 57, 76, 1, 66, 72, 33, 6, 34, 59, and 13 were used. In set 2, genes 73, 88, 39, 52, 87, 78, 84, 1, 42, 69, 62, 58, 10, 51, 38, 14, 77, 49, 36, 35, 34, 15, 65, 60, 20, 17, 61, 2, 59, 22, 81, 11, 19, 41, 5, 29, 12, 43, 7, 4, 64, 40, 74, 48, 72, 54, 68, 86, 66, 6, 67, 89, 21, 16, and 9 were used. In set 3, genes 28, 89, 35, 86, 49, 56, 69, 18, 15, 27, 13, 6, 51, 77, 8, 80, 16, 78, 43, 29, 37, 20, 9, 31, 32, 67, 48, 65, 82, 62, 76, 25, 54, 41, 90, 47, 2, 71, 87, 84, 57, 74, 61, 59, 85, 75, 10, 66, 46, 73, 24, 44, 14, 4, and 7 were used. In set 4, genes 48, 76, 17, 62, 65, 87, 19, 24, 83, 29, 55, 12, 68, 82, 73, 18, 20, 10, 81, 53, 33, 56, 34, 5, 60, 46, 16, 25, 2, 42, 6, 49, 4, 45, 88, 32, 77, 8, 1, 71, 3, 27, 72, 59, 79, 64, 11, 80, 57, 61, 75, 39, 23, 52, and 37 were used. In set 5, genes 54, 77, 74, 76, 81, 17, 25, 57, 29, 36, 55, 75, 66, 15, 2, 41, 37, 59, 12, 45, 4, 9, 69, 18, 49, 22, 42, 62, 10, 52, 48, 31, 44, 19, 79, 50, 40, 32, 89, 87, 11, 5, 73, 20, 80, 35, 70, 53, 83, 72, 88, 47, 84, 39, and 65 were used. In set 6, genes 86, 43, 75, 90, 32, 85, 38, 54, 87, 42, 73, 55, 27, 34, 11, 14, 65, 82, 77, 21, 26, 46, 83, 10, 15, 22, 66, 20, 67, 72, 35, 68, 3, 53, 44, 50, 70, 40, 30, 31, 84, 81, 62, 51, 80, 79, 59, 57, 88, 69, 2, 64, 23, 28, and 16 were used. In set 7, genes 76, 15, 53, 8, 89, 52, 20, 3, 47, 83, 45, 31, 80, 82, 4, 57, 65, 41, 29, 77, 46, 60, 24, 33, 70, 37, 12, 66, 42, 61, 63, 86, 30, 11, 40, 27, 39, 56, 9, 49, 35, 22, 10, 48, 18, 68, 58, 62, 34, 85, 84, 26, 43, 81, and 38 were used. In set 8, genes 3, 46, 11, 89, 63, 61, 26, 69, 47, 82, 27, 39, 52, 2, 70, 6, 41, 14, 36, 30, 65, 74, 28, 34, 42, 79, 59, 4, 72, 37, 66, 50, 45, 23, 73, 71, 10, 19, 78, 62, 20, 5, 56, 25, 75, 38, 13, 86, 88, 22, 32, 58, 60, 1, and 51 were used. In set 9, genes 16, 61, 85, 3, 42, 24, 55, 4, 9, 22, 28, 31, 53, 74, 25, 52, 10, 49, 2, 21, 30, 78, 54, 26, 38, 87, 35, 37, 45, 84, 83, 57, 64, 65, 68, 50, 1, 34, 75, 67, 60, 5, 7, 58, 59, 76, 27, 77, 44, 32, 6, 11, 48, 56, and 15 were used. In set 10, genes 72, 86, 46, 5, 3, 29, 54, 66, 20, 44, 41, 47, 14, 65, 83, 56, 43, 26, 49, 48, 69, 24, 45, 27, 73, 11, 40, 22, 78, 2, 39, 15, 31, 35, 77, 61, 9, 52, 37, 1, 89, 79, 60, 18, 50, 17, 88, 80, 57, 71, 12, 53, 36, 58, and 42 were used.

For 60 genes, set 1, genes 75, 54, 79, 78, 4, 48, 36, 29, 28, 32, 82, 38, 21, 8, 80, 46, 47, 57, 76, 50, 18, 68, 85, 13, 61, 65, 71, 56, 45, 58, 84, 25, 72, 43, 7, 77, 74, 69, 86, 31, 19, 63, 35, 83, 70, 3, 62, 90, 52, 87, 44, 41, 66, 12, 23, 59, 1, 10, 49, and 67 were used. In set 2, genes 6, 50, 10, 38, 29, 59, 60, 12, 74, 14, 65, 61, 54, 2, 89, 68, 9, 62, 20, 81, 70, 67, 66, 52, 45, 58, 43, 31, 86, 79, 82, 1, 42, 88, 85, 22, 87, 84, 24, 21, 5, 39, 25, 51, 40, 63, 49, 7, 35, 36, 71, 90, 47, 15, 56, 23, 83, 34, 76, and 19 were used. In set 3, genes 17, 68, 41, 53, 15, 58, 90, 21, 10, 61, 72, 44, 22, 8, 32, 47, 55, 48, 45, 3, 5, 7, 1, 4, 24, 49, 75, 54, 39, 57, 19, 70, 79, 66, 6, 60, 51, 56, 46, 14, 85, 80, 36, 31, 37, 86, 42, 84, 87, 23, 2, 81, 11, 50, 40, 52, 13, 65, 62, and 76 were used. In set 4, genes 54, 24, 50, 11, 77, 63, 84, 71, 16, 51, 78, 83, 10, 28, 31, 29, 43, 14, 30, 61, 81, 58, 4, 48, 64, 37, 18, 39, 1, 67, 45, 40, 80, 79, 8, 55, 36, 2, 32, 25, 21, 46, 73, 38, 34, 52, 49, 65, 13, 66, 6, 76, 20, 85, 15, 44, 60, 69, 86, and 88 were used. In set 5, genes 89, 22, 12, 82, 28, 14, 87, 8, 79, 48, 69, 84, 66, 43, 88, 13, 9, 50, 75, 71, 20, 36, 5, 54, 80, 62, 4, 23, 24, 60, 19, 10, 63, 81, 68, 30, 32, 52, 56, 37, 15, 83, 16, 90, 26, 44, 78, 39, 61, 59, 45, 74, 58, 86, 35, 33, 47, 57, 18, and 42 were used. In set 6, genes 41, 38, 76, 54, 12, 29, 66, 35, 68, 80, 64, 57, 46, 25, 27, 49, 86, 36, 20, 5, 16, 19, 69, 59, 48, 4, 10, 70, 17, 60, 50, 63, 18, 33, 65, 39, 23, 82, 51, 55, 8, 28, 53, 84, 67, 22, 71, 77, 13, 9, 42, 21, 62, 31, 78, 11, 89, 45, 52, and 74 were used. In set 7, genes 84, 12, 17, 10, 33, 56, 50, 61, 74, 21, 78, 11, 37, 36, 3, 5, 30, 43, 47, 54, 27, 32, 77, 51, 42, 4, 76, 71, 83, 46, 57, 73, 87, 24, 90, 8, 72, 29, 35, 66, 28, 70, 22, 39, 65, 85, 1, 82, 40, 89, 80, 58, 52, 38, 59, 86, 69, 13, 16, and 14 were used. In set 8, genes 71, 3, 44, 6, 16, 69, 34, 20, 56, 72, 5, 68, 9, 52, 49, 58, 79, 76, 2, 59, 7, 73, 51, 74, 19, 88, 60, 30, 61, 13, 89, 50, 31, 40, 81, 10, 21, 54, 45, 77, 67, 36, 46, 1, 43, 83, 55, 12, 80, 28, 41, 86, 47, 39, 53, 17, 78, 63, 87, and 48 were used. In set 9, genes 47, 30, 10, 11, 39, 23, 41, 29, 21, 36, 45, 49, 69, 1, 24, 66, 57, 12, 56, 22, 71, 9, 89, 52, 83, 28, 80, 37, 72, 67, 76, 87, 82, 5, 88, 4, 3, 68, 58, 64, 62, 46, 74, 7, 20, 15, 48, 53, 54, 63, 19, 13, 43, 32, 51, 31, 33, 27, 35, and 40 were used. In set 10, genes 75, 29, 27, 66, 15, 47, 14, 3, 12, 80, 31, 32, 41, 17, 74, 7, 57, 59, 64, 25, 13, 77, 33, 43, 81, 55, 48, 68, 30, 54, 69, 88, 62, 86, 67, 37, 20, 8, 42, 19, 70, 24, 49, 73, 23, 10, 1, 85, 89, 44, 58, 2, 11, 63, 76, 5, 53, 83, 50, and 9 were used.

For 65 genes, set 1, genes 55, 36, 14, 26, 67, 60, 28, 31, 46, 85, 16, 10, 17, 45, 73, 87, 7, 72, 90, 4, 84, 34, 78, 19, 71, 54, 29, 43, 76, 12, 35, 61, 49, 57, 89, 20, 50, 47, 86, 88, 59, 75, 15, 8, 5, 3, 32, 81, 74, 23, 41, 13, 33, 63, 77, 22, 9, 38, 64, 69, 80, 25, 1, 18, and 30 were used. In set 2, genes 32, 81, 5, 65, 79, 12, 52, 83, 2, 39, 19, 37, 44, 66, 63, 72, 56, 60, 3, 22, 70, 64, 9, 67, 15, 8, 50, 48, 71, 82, 76, 14, 28, 18, 25, 11, 29, 58, 35, 31, 10, 69, 38, 90, 80, 74, 53, 75, 4, 77, 89, 55, 57, 59, 51, 42, 41, 68, 23, 84, 45, 40, 20, 85, and 61 were used. In set 3, genes 33, 52, 22, 67, 7, 36, 40, 6, 56, 29, 48, 41, 28, 68, 83, 90, 51, 70, 60, 24, 87, 88, 18, 58, 73, 1, 17, 8, 26, 89, 38, 4, 10, 47, 75, 72, 50, 13, 23, 66, 20, 30, 12, 43, 46, 15, 16, 5, 55, 31, 63, 32, 53, 69, 39, 71, 42, 62, 57, 34, 44, 14, 25, 64, and 80 were used. In set 4, genes 30, 45, 74, 3, 13, 63, 76, 27, 46, 11, 51, 2, 20, 78, 66, 65, 43, 7, 69, 40, 28, 19, 25, 52, 26, 34, 49, 44, 60, 59, 38, 48, 85, 87, 18, 82, 15, 42, 24, 67, 61, 71, 70, 35, 68, 79, 47, 83, 80, 84, 31, 32, 9, 77, 72, 62, 8, 55, 54, 1, 58, 16, 53, 89, and 90 were used. In set 5, genes 14, 55, 53, 45, 32, 63, 49, 15, 10, 11, 47, 52, 3, 13, 71, 68, 85, 34, 66, 64, 83, 78, 28, 21, 30, 54, 29, 88, 59, 73, 26, 84, 50, 77, 65, 82, 20, 86, 19, 57, 62, 25, 43, 27, 8, 6, 87, 38, 51, 61, 56, 2, 18, 46, 44, 80, 9, 31, 36, 76, 1, 7, 33, 48, and 58 were used. In set 6, genes 66, 44, 18, 85, 54, 28, 80, 65, 25, 1, 88, 72, 74, 46, 76, 71, 24, 51, 47, 31, 21, 60, 83, 32, 3, 63, 64, 69, 52, 27, 2, 38, 34, 10, 12, 35, 77, 33, 29, 56, 67, 40, 30, 22, 49, 5, 7, 43, 17, 13, 81, 20, 79, 14, 48, 73, 53, 90, 70, 59, 19, 16, 8, 36, and 23 were used. In set 7, genes 89, 37, 48, 32, 75, 46, 90, 2, 66, 44, 55, 17, 9, 59, 68, 83, 24, 53, 19, 67, 74, 35, 72, 4, 13, 76, 15, 62, 63, 28, 51, 26, 39, 20, 18, 45, 36, 78, 41, 84, 87, 11, 80, 12, 81, 3, 50, 86, 6, 61, 73, 31, 27, 88, 42, 71, 33, 43, 60, 30, 69, 34, 21, 49, and 70 were used. In set 8, genes 84, 73, 14, 23, 36, 47, 31, 61, 57, 50, 78, 53, 90, 68, 37, 39, 75, 4, 10, 80, 35, 32, 85, 18, 81, 29, 66, 76, 54, 41, 62, 30, 58, 49, 33, 64, 45, 87, 25, 79, 20, 69, 42, 17, 88, 24, 2, 34, 16, 28, 86, 15, 7, 82, 1, 60, 11, 48, 89, 22, 77, 74, 72, 6, and 43 were used. In set 9, genes 1, 74, 39, 48, 44, 47, 3, 8, 80, 54, 16, 41, 76, 9, 85, 86, 49, 70, 52, 89, 19, 66, 43, 17, 15, 63, 29, 53, 42, 32, 30, 4, 36, 7, 77, 2, 84, 87, 28, 67, 20, 56, 65, 31, 12, 25, 40, 10, 73, 6, 83, 64, 50, 13, 22, 58, 45, 21, 57, 60, 72, 82, 26, 33, and 35 were used. In set 10, genes 18, 31, 52, 70, 48, 76, 57, 66, 10, 14, 60, 30, 67, 45, 35, 51, 1, 79, 46, 71, 3, 42, 33, 85, 4, 61, 2, 63, 87, 50, 36, 37, 90, 80, 24, 6, 77, 28, 21, 88, 17, 82, 83, 49, 75, 54, 25, 5, 7, 73, 59, 29, 69, 47, 65, 19, 15, 56, 9, 55, 58, 40, 20, 89, and 74 were used.

For 70 genes, set 1, genes 79, 50, 38, 63, 74, 71, 66, 4, 33, 1, 69, 88, 85, 18, 27, 77, 70, 65, 14, 40, 64, 29, 59, 6, 3, 9, 84, 22, 62, 60, 30, 7, 11, 13, 45, 57, 35, 72, 15, 75, 39, 36, 10, 53, 67, 80, 83, 31, 5, 25, 90, 89, 58, 23, 56, 2, 16, 41, 76, 47, 26, 43, 17, 55, 82, 87, 24, 12, 48, and 81 were used. In set 2, genes 6, 66, 68, 83, 77, 81, 21, 88, 18, 60, 50, 17, 13, 61, 14, 25, 39, 76, 75, 78, 89, 37, 87, 59, 55, 90, 9, 5, 12, 10, 43, 29, 51, 31, 46, 58, 49, 24, 52, 28, 64, 42, 8, 11, 67, 84, 70, 19, 41, 45, 71, 16, 33, 23, 34, 30, 86, 69, 4, 57, 47, 80, 20, 82, 2, 1, 56, 65, 62, and 48 were used. In set 3, genes 72, 87, 89, 53, 56, 17, 84, 60, 45, 61, 62, 76, 13, 37, 20, 21, 2, 23, 3, 57, 83, 90, 82, 49, 24, 59, 9, 48, 32, 33, 47, 42, 78, 88, 65, 52, 79, 41, 34, 19, 74, 66, 43, 27, 36, 63, 81, 44, 40, 80, 31, 86, 12, 29, 77, 67, 14, 71, 68, 1, 35, 16, 10, 30, 6, 22, 75, 55, 85, and 4 were used. In set 4, genes 70, 81, 71, 17, 8, 59, 6, 15, 52, 74, 23, 9, 19, 14, 82, 86, 27, 73, 66, 38, 22, 41, 88, 76, 47, 58, 56, 11, 55, 64, 44, 84, 62, 21, 35, 80, 36, 28, 12, 13, 4, 1, 75, 60, 5, 87, 89, 2, 50, 46, 25, 85, 37, 90, 78, 34, 24, 18, 45, 79, 77, 30, 32, 51, 57, 67, 83, 68, 54, and 29 were used. In set 5, genes 70, 23, 22, 30, 85, 48, 21, 32, 86, 84, 78, 87, 64, 40, 4, 34, 67, 19, 25, 7, 55, 42, 65, 53, 49, 83, 50, 80, 62, 16, 37, 77, 71, 54, 28, 27, 29, 18, 13, 57, 79, 56, 15, 36, 5, 24, 3, 1, 75, 90, 73, 47, 51, 88, 38, 58, 66, 81, 35, 76, 43, 46, 82, 68, 10, 14, 8, 41, 39, and 59 were used. In set 6, genes 88, 3, 40, 60, 24, 43, 62, 85, 58, 53, 39, 56, 59, 81, 71, 63, 25, 16, 22, 14, 10, 72, 89, 90, 84, 5, 33, 12, 45, 57, 70, 38, 32, 19, 44, 46, 2, 64, 8, 49, 42, 27, 37, 29, 13, 6, 28, 7, 77, 41, 17, 50, 31, 69, 26, 83, 23, 73, 80, 51, 61, 76, 82, 18, 15, 78, 67, 54, 36, and 65 were used. In set 7, genes 35, 52, 48, 42, 65, 38, 61, 79, 23, 20, 12, 8, 53, 57, 22, 54, 69, 9, 56, 43, 5, 66, 86, 49, 81, 19, 40, 45, 85, 60, 10, 50, 55, 11, 15, 73, 13, 2, 29, 59, 78, 67, 18, 80, 84, 39, 87, 90, 58, 46, 17, 32, 7, 62, 14, 34, 27, 6, 83, 70, 51, 26, 68, 21, 82, 77, 44, 47, 24, and 37 were used. In set 8, genes 40, 55, 22, 47, 86, 19, 62, 51, 25, 59, 8, 65, 48, 79, 1, 66, 17, 70, 32, 49, 23, 61, 85, 28, 36, 54, 20, 39, 83, 73, 50, 4, 81, 27, 41, 63, 15, 80, 87, 7, 46, 33, 9, 68, 56, 77, 14, 75, 82, 74, 12, 37, 16, 84, 72, 30, 2, 38, 13, 57, 76, 5, 64, 45, 89, 58, 29, 10, 78, and 90 were used. In set 9, genes 84, 16, 21, 81, 89, 60, 79, 30, 47, 69, 83, 85, 75, 52, 49, 72, 86, 3, 9, 59, 18, 55, 17, 82, 14, 23, 38, 24, 87, 65, 77, 80, 66, 19, 41, 53, 1, 34, 27, 56, 40, 67, 32, 20, 37, 70, 36, 15, 22, 8, 29, 48, 58, 45, 25, 71, 7, 4, 73, 10, 12, 2, 42, 90, 63, 43, 51, 6, 54, and 78 were used. In set 10, genes 19, 51, 29, 22, 66, 13, 32, 89, 62, 45, 65, 35, 24, 73, 55, 47, 67, 76, 69, 26, 37, 64, 53, 10, 15, 34, 79, 2, 56, 30, 3, 20, 78, 31, 75, 46, 27, 52, 6, 86, 16, 9, 54, 87, 58, 33, 61, 11, 43, 40, 74, 60, 50, 25, 80, 72, 83, 57, 38, 1, 70, 5, 7, 77, 85, 59, 88, 63, 14, and 84 were used.

For 75 genes, set 1, genes 87, 17, 52, 44, 57, 53, 78, 37, 2, 71, 9, 68, 6, 63, 50, 58, 13, 26, 16, 60, 67, 3, 32, 21, 79, 12, 77, 73, 24, 35, 80, 47, 29, 40, 30, 84, 39, 90, 11, 81, 75, 76, 89, 66, 86, 42, 34, 64, 54, 7, 41, 56, 62, 55, 46, 28, 5, 25, 27, 83, 19, 20, 49, 69, 85, 33, 18, 23, 74, 1, 10, 43, 22, 8, and 45 were used. In set 2, genes 75, 33, 52, 86, 24, 50, 70, 10, 17, 90, 28, 46, 48, 77, 47, 61, 12, 4, 83, 27, 45, 88, 35, 36, 22, 68, 73, 31, 57, 69, 65, 64, 15, 9, 54, 39, 14, 20, 67, 79, 44, 38, 78, 23, 84, 37, 66, 5, 11, 18, 41, 13, 21, 49, 16, 76, 1, 29, 53, 40, 42, 63, 25, 56, 6, 82, 71, 85, 89, 80, 34, 51, 60, 30, and 58 were used. In set 3, genes 39, 82, 36, 31, 52, 84, 30, 83, 49, 1, 44, 10, 87, 78, 77, 18, 79, 9, 73, 69, 75, 45, 14, 16, 56, 40, 58, 15, 32, 34, 42, 60, 19, 63, 47, 41, 68, 13, 61, 90, 89, 5, 46, 57, 8, 37, 66, 43, 21, 17, 11, 72, 74, 4, 33, 53, 12, 65, 50, 2, 81, 24, 62, 6, 23, 25, 88, 51, 67, 64, 7, 80, 54, 22, and 3 were used. In set 4, genes 63, 2, 5, 52, 10, 62, 75, 4, 6, 51, 29, 54, 49, 55, 36, 37, 77, 46, 44, 79, 11, 59, 38, 14, 65, 43, 48, 35, 86, 78, 73, 72, 57, 8, 16, 58, 56, 82, 60, 42, 80, 13, 9, 90, 53, 66, 21, 67, 88, 89, 45, 22, 71, 31, 84, 74, 15, 23, 26, 3, 68, 1, 39, 7, 50, 41, 40, 81, 87, 34, 18, 12, 70, 47, and 25 were used. In set 5, genes 62, 82, 46, 89, 81, 43, 57, 69, 9, 19, 18, 16, 80, 63, 72, 2, 54, 86, 44, 53, 31, 5, 1, 61, 20, 37, 58, 32, 28, 47, 34, 6, 41, 68, 15, 90, 85, 13, 23, 10, 4, 70, 76, 33, 11, 51, 35, 88, 67, 84, 8, 24, 66, 65, 26, 59, 40, 79, 64, 42, 45, 22, 17, 87, 30, 12, 27, 14, 39, 56, 38, 71, 52, 36, and 60 were used. In set 6, genes 16, 85, 19, 39, 64, 76, 44, 15, 50, 73, 27, 36, 6, 62, 54, 46, 58, 68, 28, 13, 14, 21, 86, 47, 71, 87, 18, 5, 67, 1, 65, 78, 12, 66, 43, 82, 38, 23, 75, 24, 49, 57, 17, 10, 29, 72, 22, 89, 90, 26, 42, 45, 2, 33, 41, 9, 8, 7, 69, 31, 30, 79, 80, 84, 55, 35, 20, 70, 83, 48, 88, 60, 25, 74, and 63 were used. In set 7, genes 24, 66, 86, 48, 63, 51, 74, 37, 2, 82, 77, 22, 72, 21, 11, 90, 80, 55, 76, 68, 34, 42, 29, 62, 46, 39, 56, 31, 47, 28, 16, 38, 44, 52, 1, 43, 14, 20, 64, 83, 78, 58, 12, 18, 84, 67, 75, 85, 36, 25, 50, 49, 40, 33, 23, 45, 41, 73, 88, 59, 17, 32, 70, 13, 60, 57, 3, 7, 54, 4, 8, 53, 26, 15, and 69 were used. In set 8, genes 80, 38, 59, 41, 85, 44, 12, 22, 39, 17, 52, 24, 32, 62, 18, 8, 78, 74, 9, 66, 76, 14, 3, 16, 40, 28, 48, 58, 54, 29, 43, 5, 81, 77, 86, 23, 75, 82, 34, 7, 51, 64, 4, 6, 72, 61, 37, 84, 45, 33, 71, 19, 67, 88, 1, 35, 47, 83, 25, 49, 11, 42, 50, 70, 2, 46, 15, 26, 27, 68, 57, 65, 13, 53, and 90 were used. In set 9, genes 4, 66, 28, 44, 20, 34, 12, 85, 6, 17, 88, 8, 39, 65, 22, 19, 10, 48, 63, 23, 33, 13, 47, 81, 79, 89, 64, 53, 87, 11, 46, 74, 14, 70, 37, 62, 30, 7, 71, 76, 50, 59, 77, 51, 15, 68, 55, 72, 83, 82, 78, 54, 25, 21, 27, 41, 69, 9, 58, 3, 31, 75, 84, 26, 86, 49, 18, 42, 61, 45, 16, 2, 24, 80, and 73 were used. In set 10, genes 78, 47, 32, 30, 46, 6, 2, 64, 11, 27, 85, 22, 79, 63, 80, 39, 90, 65, 71, 72, 21, 26, 58, 15, 16, 23, 81, 1, 44, 43, 40, 55, 13, 19, 25, 83, 41, 18, 53, 68, 37, 20, 49, 69, 33, 61, 38, 28, 60, 45, 17, 82, 24, 4, 86, 89, 36, 51, 84, 31, 14, 88, 59, 76, 48, 5, 35, 75, 74, 7, 67, 62, 52, 56, and 54 were used.

For 80 genes, set 1, genes 29, 80, 5, 50, 63, 3, 1, 55, 38, 48, 58, 30, 86, 82, 83, 6, 23, 2, 41, 60, 54, 69, 15, 34, 64, 10, 27, 70, 28, 44, 8, 68, 56, 14, 36, 17, 73, 13, 88, 42, 72, 59, 67, 71, 26, 53, 37, 24, 79, 62, 52, 74, 4, 40, 47, 19, 78, 11, 76, 31, 90, 12, 87, 89, 75, 66, 81, 16, 49, 65, 57, 84, 46, 20, and 21 were used. In set 2, genes 15, 21, 70, 5, 79, 85, 84, 53, 69, 33, 28, 14, 75, 76, 58, 48, 13, 45, 51, 88, 25, 74, 39, 71, 64, 9, 60, 44, 78, 7, 8, 3, 32, 89, 73, 1, 4, 29, 41, 17, 46, 57, 72, 20, 86, 47, 49, 87, 55, 19, 37, 27, 80, 62, 54, 18, 52, 67, 63, 77, 65, 24, 31, 26, 83, 2, 22, 90, 50, 12, 16, 35, 11, 10, and 56 were used. In set 3, genes 41, 4, 59, 73, 29, 22, 60, 45, 70, 10, 64, 21, 81, 36, 52, 67, 54, 38, 65, 90, 27, 87, 28, 7, 74, 43, 56, 75, 9, 35, 42, 20, 72, 47, 14, 63, 18, 68, 23, 69, 8, 50, 89, 3, 11, 82, 39, 80, 46, 16, 53, 58, 25, 79, 49, 76, 37, 30, 78, 83, 2, 84, 57, 88, 6, 32, 12, 71, 15, 55, 48, 34, 62, 61, and 13 were used. In set 4, genes 23, 31, 53, 90, 3, 40, 34, 6, 1, 83, 9, 60, 56, 50, 44, 85, 51, 35, 43, 80, 65, 46, 38, 88, 17, 54, 87, 10, 45, 42, 75, 68, 63, 58, 36, 64, 67, 77, 21, 47, 30, 59, 14, 49, 70, 66, 72, 74, 27, 61, 19, 81, 20, 25, 33, 57, 62, 76, 55, 78, 84, 16, 69, 37, 79, 29, 39, 32, 15, 5, 2, 12, 71, 11, and 73 were used. In set 5, genes 29, 71, 21, 60, 43, 78, 55, 61, 51, 90, 10, 37, 35, 53, 28, 62, 15, 1, 31, 67, 48, 36, 75, 27, 63, 87, 24, 32, 54, 79, 16, 70, 64, 40, 47, 41, 17, 38, 3, 45, 81, 68, 72, 56, 77, 8, 13, 34, 57, 26, 73, 14, 6, 82, 4, 58, 89, 30, 7, 74, 69, 88, 20, 5, 46, 2, 11, 49, 50, 23, 33, 42, 83, 52, and 86 were used. In set 6, genes 39, 54, 30, 24, 80, 10, 21, 7, 14, 69, 38, 83, 52, 65, 46, 42, 66, 36, 61, 16, 50, 33, 2, 73, 13, 81, 48, 8, 6, 41, 12, 25, 43, 79, 35, 26, 89, 75, 60, 67, 82, 45, 20, 90, 68, 77, 58, 34, 18, 47, 22, 84, 4, 57, 32, 5, 19, 59, 86, 74, 1, 31, 62, 85, 29, 53, 88, 28, 40, 37, 63, 15, 64, 49, and 55 were used. In set 7, genes 21, 68, 81, 50, 36, 6, 80, 76, 90, 74, 12, 79, 34, 53, 1, 4, 5, 41, 56, 47, 15, 63, 11, 14, 7, 78, 57, 65, 73, 20, 8, 64, 84, 30, 3, 13, 52, 49, 27, 86, 60, 72, 62, 29, 75, 40, 32, 2, 82, 33, 10, 24, 51, 17, 46, 38, 19, 37, 28, 69, 61, 85, 88, 22, 48, 89, 18, 25, 71, 58, 31, 35, 26, 55, and 44 were used. In set 8, genes 30, 64, 67, 79, 52, 71, 13, 3, 22, 8, 75, 41, 65, 21, 60, 36, 49, 84, 33, 29, 57, 86, 15, 12, 85, 63, 6, 20, 66, 53, 51, 90, 87, 55, 11, 32, 31, 61, 78, 58, 42, 48, 5, 1, 17, 50, 70, 76, 25, 45, 2, 73, 28, 14, 89, 56, 39, 44, 7, 74, 16, 72, 35, 19, 47, 27, 43, 83, 68, 26, 18, 37, 69, 54, and 23 were used. In set 9, genes 79, 85, 48, 29, 23, 31, 62, 37, 5, 33, 3, 19, 53, 9, 36, 18, 58, 17, 81, 46, 8, 35, 66, 87, 14, 30, 74, 77, 21, 40, 75, 43, 42, 15, 39, 70, 60, 13, 10, 2, 72, 44, 45, 38, 4, 25, 84, 68, 50, 24, 7, 27, 82, 55, 80, 32, 89, 57, 6, 69, 83, 28, 56, 22, 16, 1, 41, 63, 26, 78, 12, 59, 64, 61, and 11 were used. In set 10, genes 45, 9, 24, 85, 68, 80, 73, 17, 56, 7, 8, 5, 69, 58, 37, 44, 21, 29, 50, 15, 53, 25, 40, 88, 36, 32, 59, 75, 49, 35, 43, 67, 83, 31, 51, 28, 60, 77, 30, 74, 22, 41, 42, 64, 61, 23, 90, 13, 33, 11, 16, 20, 46, 66, 6, 87, 39, 47, 65, 3, 82, 10, 72, 34, 18, 1, 38, 57, 79, 71, 26, 27, 19, 48, and 76 were used.

For 85 genes, set 1, genes 62, 19, 38, 77, 64, 49, 14, 16, 47, 73, 28, 3, 54, 78, 70, 12, 75, 35, 15, 40, 21, 60, 58, 86, 83, 33, 66, 59, 44, 45, 56, 9, 5, 81, 72, 68, 27, 37, 71, 52, 48, 36, 79, 6, 41, 74, 22, 46, 2, 20, 34, 13, 55, 53, 10, 88, 57, 61, 4, 39, 24, 85, 76, 87, 65, 25, 23, 90, 32, 26, 80, 63, 89, 82, and 7 were used. In set 2, genes 72, 30, 36, 64, 47, 57, 67, 20, 58, 1, 6, 61, 71, 32, 42, 53, 87, 65, 25, 17, 9, 60, 83, 12, 51, 8, 37, 75, 59, 89, 85, 22, 44, 19, 63, 7, 62, 13, 81, 41, 79, 43, 49, 4, 34, 68, 88, 74, 28, 31, 10, 39, 11, 55, 15, 5, 69, 50, 66, 18, 77, 46, 76, 33, 3, 35, 38, 14, 40, 86, 54, 23, 24, 48, and 78 were used. In set 3, genes 5, 67, 57, 18, 12, 42, 43, 71, 50, 19, 26, 51, 52, 32, 74, 88, 46, 2, 9, 77, 30, 58, 69, 81, 35, 87, 90, 34, 22, 15, 84, 44, 8, 3, 47, 60, 55, 66, 33, 20, 86, 39, 16, 37, 85, 73, 4, 13, 56, 27, 65, 76, 49, 54, 75, 31, 68, 82, 23, 62, 7, 53, 78, 36, 64, 40, 45, 6, 70, 17, 79, 10, 21, 48, and 89 were used. In set 4, genes 67, 47, 68, 38, 50, 82, 54, 56, 64, 49, 63, 14, 22, 7, 25, 12, 57, 85, 88, 5, 28, 23, 77, 44, 80, 89, 83, 20, 81, 73, 11, 17, 76, 75, 32, 34, 55, 62, 21, 6, 30, 10, 71, 39, 36, 74, 42, 60, 43, 8, 59, 58, 65, 3, 61, 72, 70, 79, 16, 13, 18, 19, 45, 4, 84, 1, 87, 26, 46, 40, 37, 78, 15, 69, and 41 were used. In set 5, genes 82, 42, 35, 86, 14, 37, 39, 30, 41, 60, 44, 9, 12, 34, 50, 68, 5, 29, 46, 19, 11, 28, 48, 3, 20, 77, 67, 57, 88, 55, 32, 78, 51, 71, 47, 63, 6, 10, 45, 70, 8, 81, 18, 43, 69, 79, 21, 13, 66, 59, 33, 1, 31, 74, 36, 2, 24, 54, 23, 85, 72, 73, 80, 64, 84, 7, 38, 87, 58, 75, 22, 65, 15, 53, and 52 were used. In set 6, genes 55, 2, 11, 72, 4, 85, 43, 18, 46, 27, 80, 69, 9, 31, 39, 5, 81, 22, 32, 3, 36, 17, 83, 37, 90, 38, 87, 44, 56, 66, 13, 6, 28, 77, 54, 79, 41, 78, 47, 29, 8, 21, 63, 64, 73, 48, 14, 34, 82, 70, 30, 58, 84, 24, 26, 68, 1, 65, 60, 42, 33, 20, 7, 75, 12, 57, 59, 16, 74, 88, 23, 49, 50, 40, and 71 were used. In set 7, genes 18, 40, 66, 35, 20, 85, 12, 19, 86, 26, 36, 89, 84, 88, 74, 15, 33, 75, 50, 16, 49, 32, 38, 31, 2, 27, 87, 68, 69, 53, 60, 79, 7, 21, 63, 17, 90, 30, 29, 11, 56, 25, 58, 62, 48, 8, 45, 9, 72, 64, 28, 76, 3, 78, 46, 1, 10, 34, 43, 83, 5, 52, 14, 65, 51, 41, 22, 44, 61, 24, 70, 54, 59, 77, and 13 were used. In set 8, genes 35, 40, 80, 57, 23, 28, 9, 83, 13, 47, 82, 36, 86, 44, 90, 55, 30, 22, 12, 42, 38, 49, 45, 8, 87, 17, 52, 3, 33, 15, 32, 21, 76, 58, 7, 53, 20, 67, 19, 29, 85, 68, 71, 39, 24, 25, 84, 4, 6, 75, 63, 73, 5, 18, 31, 48, 65, 41, 60, 37, 88, 72, 1, 46, 79, 16, 78, 10, 77, 34, 66, 56, 61, 70, and 2 were used. In set 9, genes 51, 59, 73, 9, 79, 21, 39, 67, 71, 68, 28, 65, 85, 30, 41, 61, 29, 8, 16, 78, 34, 1, 77, 90, 45, 33, 60, 89, 49, 56, 43, 62, 83, 6, 11, 18, 50, 66, 47, 19, 4, 22, 13, 27, 86, 26, 20, 17, 52, 10, 70, 54, 42, 53, 24, 76, 81, 75, 38, 64, 74, 36, 48, 32, 82, 44, 37, 57, 72, 35, 7, 14, 15, 3, and 23 were used. In set 10, genes 18, 85, 61, 1, 52, 87, 42, 13, 88, 66, 46, 57, 50, 36, 75, 39, 14, 27, 54, 20, 53, 10, 4, 30, 37, 43, 79, 80, 40, 84, 76, 45, 60, 74, 12, 31, 15, 44, 48, 3, 56, 11, 68, 19, 86, 72, 6, 9, 21, 70, 34, 83, 89, 5, 69, 64, 22, 24, 63, 65, 55, 8, 41, 28, 2, 16, 35, 77, 26, 47, 90, 49, 59, 23, and 17 were used.

Example 5 PCR Based Detection

As noted above, the determination or measurement of gene expression may be performed by PCR, such as the use of quantitative PCR. Detecting expression of 50 or more expressed sequences in the human genome may be used in such embodiments of the invention. Additionally, expression levels of 50 or more gene sequences in the set of 74, the set of 90, or a combination set of the two (with a total of 126 gene sequences given the presence of 38 gene sequences in common between the two sets) may also be used. The invention contemplates the use of quantitative PCR to measure expression levels, as described above, of 87 gene sequences (or 50 or more sequences thereof), all of which are present in either the set of 74 or the set of 90. Of the 87 gene sequences, 60 are present in the set of 74, and 63 are present in the set of 90. The identifiers/accession numbers of the 87 gene sequences are AA456140, AA745593, AA765597, AA782845, AA865917, AA946776, AA993639, AB038160, AF104032, AF133587, AF301598, AF332224, AI041545, AI147926, AI309080, AI341378, AI457360, AI620495, AI632869, AI683181, AI685931, AI802118, AI804745, AI952953, AI985118, AJ000388, AK025181, AK027147, AK054605, AL023657, AL039118, AL110274, AL157475, AW118445, AW194680, AW291189, AW298545, AW445220, AW473119, AY033998, BC000045, BC001293, BC001504, BC001639, BC002551, BC004331, BC004453, BC005364, BC006537, BC006811, BC006819, BC008764, BC008765, BC009084, BC009237, BC010626, BC011949, BC012926, BC013117, BC015754, BC017586, BE552004, BE962007, BF224381, BF437393, BF446419, BF592799, BI493248, H05388, H07885, H09748, M95585, N64339, NM_(—)000065, NM_(—)001337, NM_(—)003914, NM_(—)004062, NM_(—)004063, NM_(—)004496, NM_(—)006115, NM_(—)019894, NM_(—)033229, R15881, R45389, R61469, X69699, and X96757.

The use of from 50 to all of these sequences in the practice of the invention may include the use of expression levels measured for reference gene sequences as described herein. In some embodiments, the reference gene sequences are one or more of the 8 disclosed herein. The invention contemplates the use of one or more of the reference sequences identified by AF308803, AL137727, BC003043, BC006091, and BC016680 in PCR or QPCR based embodiments of the invention. Of course all 5 of these reference sequences may also be used.

All references cited herein, including patents, patent applications, and publications, are hereby incorporated by reference in their entireties, whether previously specifically incorporated or not.

Having now fully described this invention, it will be appreciated by those skilled in the art that the same can be performed within a wide range of equivalent parameters, concentrations, and conditions without departing from the spirit and scope of the invention and without undue experimentation.

While this invention has been described in connection with specific embodiments thereof, it will be understood that it is capable of further modifications. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains and as may be applied to the essential features hereinbefore set forth. 

1. A method of classifying a cell containing sample as containing tumor cells of a type of tissue, said method comprising detecting expressed RNA or cDNA copies thereof to determine the expression levels of 50 or more transcribed sequences from cells in a cell containing sample obtained from a human subject, comparing said expression levels of 50 or more transcribed sequences to expression levels of the same 50 or more transcribed sequences in a plurality of known tumor tissue types, and classifying the sample as containing, or not containing, tumor cells of a tumor from among said plurality of known tumor types, wherein the 50 or more transcribed sequences comprises at least one transcribed sequence of a gene sequence selected from AA456140, AA745593, AA765597, AA782845, AA865917, AA946776, AA993639, AB038160, AF104032, AF133587, AF301598, AF332224, AI041545, AI147926, AI309080, AI341378, AI457360, AI620495, AI632869, AI683181, AI685931, AI802118, AI804745, AI952953, AI985118, AJ000388, AK025181, AK027147, AK054605, AL023657, AL039118, AL110274, AL157475, AW118445, AW194680, AW291189, AW298545, AW445220, AW473119, AY033998, BC000045, BC001293, BC001504, BC001639, BC002551, BC004331, BC004453, BC005364, BC006537, BC006811, BC006819, BC008764, BC008765, BC009084, BC009237, BC010626, BC011949, BC012926, BC013117, BC015754, BC017586, BE552004, BE962007, BF224381, BF437393, BF446419, BF592799, BI493248, H05388, H07885, H09748, M95585, N64339, NM_(—)000065, NM_(—)001337, NM_(—)003914, NM_(—)004062, NM_(—)004063, NM_(—)004496, NM_(—)006115, NM_(—)019894, NM_(—)033229, R15881, R45389, R61469, X69699, and X96757.
 2. The method of claim 1, wherein the 50 or more transcribed sequences includes at least one transcribed sequence of each gene sequence from the following: AA456140, AA745593, AA765597, AA782845, AA865917, AA946776, AA993639, AB038160, AF104032, AF133587, AF301598, AF332224, AI041545, AI147926, AI309080, AI341378, AI457360, AI620495, AI632869, AI683181, AI685931, AI802118, AI804745, AI952953, AI985118, AJ000388, AK025181, AK027147, AK054605, AL023657, AL039118, AL110274, AL157475, AW118445, AW194680, AW291189, AW298545, AW445220, AW473119, AY033998, BC000045, BC001293, BC001504, BC001639, BC002551, BC004331, BC004453, BC005364, BC006537, BC006811, BC006819, BC008764, BC008765, BC009084, BC009237, BC010626, BC011949, BC012926, BC013117, BC015754, BC017586, BE552004, BE962007, BF224381, BF437393, BF446419, BF592799, BI493248, H05388, H07885, H09748, M95585, N64339, NM_(—)000065, NM_(—)001337, NM_(—)003914, NM_(—)004062, NM_(—)004063, NM_(—)004496, NM_(—)006115, NM_(—)019894, NM_(—)033229, R15881, R45389, R61469, X69699, and X96757.
 3. The method of claim 1 wherein said determining comprises measurement in comparison to one or more reference transcribed sequences.
 4. The method of claim 1 wherein said determining comprises amplification of all or part of the transcribed sequences, or reverse transcription and labeling RNA corresponding to said transcribed sequences.
 5. The method of claim 4 wherein said amplification comprises linear RNA amplification or quantitative PCR.
 6. The method of claim 1, wherein said sample is a formalin fixed, paraffin embedded (FFPE) sample. 